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• Guide to Experimental Design | Overview, Steps, & Examples

Guide to Experimental Design | Overview, 5 steps & Examples

Published on December 3, 2019 by Rebecca Bevans . Revised on June 21, 2023.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design create a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying.

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead. This minimizes several types of research bias, particularly sampling bias , survivorship bias , and attrition bias as time passes.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, other interesting articles, frequently asked questions about experiments.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

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Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalized and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomized design vs a randomized block design .
• A between-subjects design vs a within-subjects design .

Randomization

An experiment can be completely randomized or randomized within blocks (aka strata):

• In a completely randomized design , every subject is assigned to a treatment group at random.
• In a randomized block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomization isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs. within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomizing or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimize research bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalized to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

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Home » Experimental Design – Types, Methods, Guide

Experimental Design – Types, Methods, Guide

Table of Contents

Experimental Design

Experimental design is a process of planning and conducting scientific experiments to investigate a hypothesis or research question. It involves carefully designing an experiment that can test the hypothesis, and controlling for other variables that may influence the results.

Experimental design typically includes identifying the variables that will be manipulated or measured, defining the sample or population to be studied, selecting an appropriate method of sampling, choosing a method for data collection and analysis, and determining the appropriate statistical tests to use.

Types of Experimental Design

Here are the different types of experimental design:

Completely Randomized Design

In this design, participants are randomly assigned to one of two or more groups, and each group is exposed to a different treatment or condition.

Randomized Block Design

This design involves dividing participants into blocks based on a specific characteristic, such as age or gender, and then randomly assigning participants within each block to one of two or more treatment groups.

Factorial Design

In a factorial design, participants are randomly assigned to one of several groups, each of which receives a different combination of two or more independent variables.

Repeated Measures Design

In this design, each participant is exposed to all of the different treatments or conditions, either in a random order or in a predetermined order.

Crossover Design

This design involves randomly assigning participants to one of two or more treatment groups, with each group receiving one treatment during the first phase of the study and then switching to a different treatment during the second phase.

Split-plot Design

In this design, the researcher manipulates one or more variables at different levels and uses a randomized block design to control for other variables.

Nested Design

This design involves grouping participants within larger units, such as schools or households, and then randomly assigning these units to different treatment groups.

Laboratory Experiment

Laboratory experiments are conducted under controlled conditions, which allows for greater precision and accuracy. However, because laboratory conditions are not always representative of real-world conditions, the results of these experiments may not be generalizable to the population at large.

Field Experiment

Field experiments are conducted in naturalistic settings and allow for more realistic observations. However, because field experiments are not as controlled as laboratory experiments, they may be subject to more sources of error.

Experimental Design Methods

Experimental design methods refer to the techniques and procedures used to design and conduct experiments in scientific research. Here are some common experimental design methods:

Randomization

This involves randomly assigning participants to different groups or treatments to ensure that any observed differences between groups are due to the treatment and not to other factors.

Control Group

The use of a control group is an important experimental design method that involves having a group of participants that do not receive the treatment or intervention being studied. The control group is used as a baseline to compare the effects of the treatment group.

Blinding involves keeping participants, researchers, or both unaware of which treatment group participants are in, in order to reduce the risk of bias in the results.

Counterbalancing

This involves systematically varying the order in which participants receive treatments or interventions in order to control for order effects.

Replication

Replication involves conducting the same experiment with different samples or under different conditions to increase the reliability and validity of the results.

This experimental design method involves manipulating multiple independent variables simultaneously to investigate their combined effects on the dependent variable.

This involves dividing participants into subgroups or blocks based on specific characteristics, such as age or gender, in order to reduce the risk of confounding variables.

Data Collection Method

Experimental design data collection methods are techniques and procedures used to collect data in experimental research. Here are some common experimental design data collection methods:

Direct Observation

This method involves observing and recording the behavior or phenomenon of interest in real time. It may involve the use of structured or unstructured observation, and may be conducted in a laboratory or naturalistic setting.

Self-report Measures

Self-report measures involve asking participants to report their thoughts, feelings, or behaviors using questionnaires, surveys, or interviews. These measures may be administered in person or online.

Behavioral Measures

Behavioral measures involve measuring participants’ behavior directly, such as through reaction time tasks or performance tests. These measures may be administered using specialized equipment or software.

Physiological Measures

Physiological measures involve measuring participants’ physiological responses, such as heart rate, blood pressure, or brain activity, using specialized equipment. These measures may be invasive or non-invasive, and may be administered in a laboratory or clinical setting.

Archival Data

Archival data involves using existing records or data, such as medical records, administrative records, or historical documents, as a source of information. These data may be collected from public or private sources.

Computerized Measures

Computerized measures involve using software or computer programs to collect data on participants’ behavior or responses. These measures may include reaction time tasks, cognitive tests, or other types of computer-based assessments.

Video Recording

Video recording involves recording participants’ behavior or interactions using cameras or other recording equipment. This method can be used to capture detailed information about participants’ behavior or to analyze social interactions.

Data Analysis Method

Experimental design data analysis methods refer to the statistical techniques and procedures used to analyze data collected in experimental research. Here are some common experimental design data analysis methods:

Descriptive Statistics

Descriptive statistics are used to summarize and describe the data collected in the study. This includes measures such as mean, median, mode, range, and standard deviation.

Inferential Statistics

Inferential statistics are used to make inferences or generalizations about a larger population based on the data collected in the study. This includes hypothesis testing and estimation.

Analysis of Variance (ANOVA)

ANOVA is a statistical technique used to compare means across two or more groups in order to determine whether there are significant differences between the groups. There are several types of ANOVA, including one-way ANOVA, two-way ANOVA, and repeated measures ANOVA.

Regression Analysis

Regression analysis is used to model the relationship between two or more variables in order to determine the strength and direction of the relationship. There are several types of regression analysis, including linear regression, logistic regression, and multiple regression.

Factor Analysis

Factor analysis is used to identify underlying factors or dimensions in a set of variables. This can be used to reduce the complexity of the data and identify patterns in the data.

Structural Equation Modeling (SEM)

SEM is a statistical technique used to model complex relationships between variables. It can be used to test complex theories and models of causality.

Cluster Analysis

Cluster analysis is used to group similar cases or observations together based on similarities or differences in their characteristics.

Time Series Analysis

Time series analysis is used to analyze data collected over time in order to identify trends, patterns, or changes in the data.

Multilevel Modeling

Multilevel modeling is used to analyze data that is nested within multiple levels, such as students nested within schools or employees nested within companies.

Applications of Experimental Design 

Experimental design is a versatile research methodology that can be applied in many fields. Here are some applications of experimental design:

• Medical Research: Experimental design is commonly used to test new treatments or medications for various medical conditions. This includes clinical trials to evaluate the safety and effectiveness of new drugs or medical devices.
• Agriculture : Experimental design is used to test new crop varieties, fertilizers, and other agricultural practices. This includes randomized field trials to evaluate the effects of different treatments on crop yield, quality, and pest resistance.
• Environmental science: Experimental design is used to study the effects of environmental factors, such as pollution or climate change, on ecosystems and wildlife. This includes controlled experiments to study the effects of pollutants on plant growth or animal behavior.
• Psychology : Experimental design is used to study human behavior and cognitive processes. This includes experiments to test the effects of different interventions, such as therapy or medication, on mental health outcomes.
• Engineering : Experimental design is used to test new materials, designs, and manufacturing processes in engineering applications. This includes laboratory experiments to test the strength and durability of new materials, or field experiments to test the performance of new technologies.
• Education : Experimental design is used to evaluate the effectiveness of teaching methods, educational interventions, and programs. This includes randomized controlled trials to compare different teaching methods or evaluate the impact of educational programs on student outcomes.
• Marketing : Experimental design is used to test the effectiveness of marketing campaigns, pricing strategies, and product designs. This includes experiments to test the impact of different marketing messages or pricing schemes on consumer behavior.

Examples of Experimental Design 

Here are some examples of experimental design in different fields:

• Example in Medical research : A study that investigates the effectiveness of a new drug treatment for a particular condition. Patients are randomly assigned to either a treatment group or a control group, with the treatment group receiving the new drug and the control group receiving a placebo. The outcomes, such as improvement in symptoms or side effects, are measured and compared between the two groups.
• Example in Education research: A study that examines the impact of a new teaching method on student learning outcomes. Students are randomly assigned to either a group that receives the new teaching method or a group that receives the traditional teaching method. Student achievement is measured before and after the intervention, and the results are compared between the two groups.
• Example in Environmental science: A study that tests the effectiveness of a new method for reducing pollution in a river. Two sections of the river are selected, with one section treated with the new method and the other section left untreated. The water quality is measured before and after the intervention, and the results are compared between the two sections.
• Example in Marketing research: A study that investigates the impact of a new advertising campaign on consumer behavior. Participants are randomly assigned to either a group that is exposed to the new campaign or a group that is not. Their behavior, such as purchasing or product awareness, is measured and compared between the two groups.
• Example in Social psychology: A study that examines the effect of a new social intervention on reducing prejudice towards a marginalized group. Participants are randomly assigned to either a group that receives the intervention or a control group that does not. Their attitudes and behavior towards the marginalized group are measured before and after the intervention, and the results are compared between the two groups.

When to use Experimental Research Design 

Experimental research design should be used when a researcher wants to establish a cause-and-effect relationship between variables. It is particularly useful when studying the impact of an intervention or treatment on a particular outcome.

Here are some situations where experimental research design may be appropriate:

• When studying the effects of a new drug or medical treatment: Experimental research design is commonly used in medical research to test the effectiveness and safety of new drugs or medical treatments. By randomly assigning patients to treatment and control groups, researchers can determine whether the treatment is effective in improving health outcomes.
• When evaluating the effectiveness of an educational intervention: An experimental research design can be used to evaluate the impact of a new teaching method or educational program on student learning outcomes. By randomly assigning students to treatment and control groups, researchers can determine whether the intervention is effective in improving academic performance.
• When testing the effectiveness of a marketing campaign: An experimental research design can be used to test the effectiveness of different marketing messages or strategies. By randomly assigning participants to treatment and control groups, researchers can determine whether the marketing campaign is effective in changing consumer behavior.
• When studying the effects of an environmental intervention: Experimental research design can be used to study the impact of environmental interventions, such as pollution reduction programs or conservation efforts. By randomly assigning locations or areas to treatment and control groups, researchers can determine whether the intervention is effective in improving environmental outcomes.
• When testing the effects of a new technology: An experimental research design can be used to test the effectiveness and safety of new technologies or engineering designs. By randomly assigning participants or locations to treatment and control groups, researchers can determine whether the new technology is effective in achieving its intended purpose.

How to Conduct Experimental Research

Here are the steps to conduct Experimental Research:

• Identify a Research Question : Start by identifying a research question that you want to answer through the experiment. The question should be clear, specific, and testable.
• Develop a Hypothesis: Based on your research question, develop a hypothesis that predicts the relationship between the independent and dependent variables. The hypothesis should be clear and testable.
• Design the Experiment : Determine the type of experimental design you will use, such as a between-subjects design or a within-subjects design. Also, decide on the experimental conditions, such as the number of independent variables, the levels of the independent variable, and the dependent variable to be measured.
• Select Participants: Select the participants who will take part in the experiment. They should be representative of the population you are interested in studying.
• Randomly Assign Participants to Groups: If you are using a between-subjects design, randomly assign participants to groups to control for individual differences.
• Conduct the Experiment : Conduct the experiment by manipulating the independent variable(s) and measuring the dependent variable(s) across the different conditions.
• Analyze the Data: Analyze the data using appropriate statistical methods to determine if there is a significant effect of the independent variable(s) on the dependent variable(s).
• Draw Conclusions: Based on the data analysis, draw conclusions about the relationship between the independent and dependent variables. If the results support the hypothesis, then it is accepted. If the results do not support the hypothesis, then it is rejected.
• Communicate the Results: Finally, communicate the results of the experiment through a research report or presentation. Include the purpose of the study, the methods used, the results obtained, and the conclusions drawn.

Purpose of Experimental Design 

The purpose of experimental design is to control and manipulate one or more independent variables to determine their effect on a dependent variable. Experimental design allows researchers to systematically investigate causal relationships between variables, and to establish cause-and-effect relationships between the independent and dependent variables. Through experimental design, researchers can test hypotheses and make inferences about the population from which the sample was drawn.

Experimental design provides a structured approach to designing and conducting experiments, ensuring that the results are reliable and valid. By carefully controlling for extraneous variables that may affect the outcome of the study, experimental design allows researchers to isolate the effect of the independent variable(s) on the dependent variable(s), and to minimize the influence of other factors that may confound the results.

Experimental design also allows researchers to generalize their findings to the larger population from which the sample was drawn. By randomly selecting participants and using statistical techniques to analyze the data, researchers can make inferences about the larger population with a high degree of confidence.

Overall, the purpose of experimental design is to provide a rigorous, systematic, and scientific method for testing hypotheses and establishing cause-and-effect relationships between variables. Experimental design is a powerful tool for advancing scientific knowledge and informing evidence-based practice in various fields, including psychology, biology, medicine, engineering, and social sciences.

Advantages of Experimental Design 

Experimental design offers several advantages in research. Here are some of the main advantages:

• Control over extraneous variables: Experimental design allows researchers to control for extraneous variables that may affect the outcome of the study. By manipulating the independent variable and holding all other variables constant, researchers can isolate the effect of the independent variable on the dependent variable.
• Establishing causality: Experimental design allows researchers to establish causality by manipulating the independent variable and observing its effect on the dependent variable. This allows researchers to determine whether changes in the independent variable cause changes in the dependent variable.
• Replication : Experimental design allows researchers to replicate their experiments to ensure that the findings are consistent and reliable. Replication is important for establishing the validity and generalizability of the findings.
• Random assignment: Experimental design often involves randomly assigning participants to conditions. This helps to ensure that individual differences between participants are evenly distributed across conditions, which increases the internal validity of the study.
• Precision : Experimental design allows researchers to measure variables with precision, which can increase the accuracy and reliability of the data.
• Generalizability : If the study is well-designed, experimental design can increase the generalizability of the findings. By controlling for extraneous variables and using random assignment, researchers can increase the likelihood that the findings will apply to other populations and contexts.

Limitations of Experimental Design

Experimental design has some limitations that researchers should be aware of. Here are some of the main limitations:

• Artificiality : Experimental design often involves creating artificial situations that may not reflect real-world situations. This can limit the external validity of the findings, or the extent to which the findings can be generalized to real-world settings.
• Ethical concerns: Some experimental designs may raise ethical concerns, particularly if they involve manipulating variables that could cause harm to participants or if they involve deception.
• Participant bias : Participants in experimental studies may modify their behavior in response to the experiment, which can lead to participant bias.
• Limited generalizability: The conditions of the experiment may not reflect the complexities of real-world situations. As a result, the findings may not be applicable to all populations and contexts.
• Cost and time : Experimental design can be expensive and time-consuming, particularly if the experiment requires specialized equipment or if the sample size is large.
• Researcher bias : Researchers may unintentionally bias the results of the experiment if they have expectations or preferences for certain outcomes.
• Lack of feasibility : Experimental design may not be feasible in some cases, particularly if the research question involves variables that cannot be manipulated or controlled.

About the author

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19+ Experimental Design Examples (Methods + Types)

Ever wondered how scientists discover new medicines, psychologists learn about behavior, or even how marketers figure out what kind of ads you like? Well, they all have something in common: they use a special plan or recipe called an "experimental design."

Imagine you're baking cookies. You can't just throw random amounts of flour, sugar, and chocolate chips into a bowl and hope for the best. You follow a recipe, right? Scientists and researchers do something similar. They follow a "recipe" called an experimental design to make sure their experiments are set up in a way that the answers they find are meaningful and reliable.

Experimental design is the roadmap researchers use to answer questions. It's a set of rules and steps that researchers follow to collect information, or "data," in a way that is fair, accurate, and makes sense.

Long ago, people didn't have detailed game plans for experiments. They often just tried things out and saw what happened. But over time, people got smarter about this. They started creating structured plans—what we now call experimental designs—to get clearer, more trustworthy answers to their questions.

In this article, we'll take you on a journey through the world of experimental designs. We'll talk about the different types, or "flavors," of experimental designs, where they're used, and even give you a peek into how they came to be.

What Is Experimental Design?

Alright, before we dive into the different types of experimental designs, let's get crystal clear on what experimental design actually is.

Imagine you're a detective trying to solve a mystery. You need clues, right? Well, in the world of research, experimental design is like the roadmap that helps you find those clues. It's like the game plan in sports or the blueprint when you're building a house. Just like you wouldn't start building without a good blueprint, researchers won't start their studies without a strong experimental design.

So, why do we need experimental design? Think about baking a cake. If you toss ingredients into a bowl without measuring, you'll end up with a mess instead of a tasty dessert.

Similarly, in research, if you don't have a solid plan, you might get confusing or incorrect results. A good experimental design helps you ask the right questions ( think critically ), decide what to measure ( come up with an idea ), and figure out how to measure it (test it). It also helps you consider things that might mess up your results, like outside influences you hadn't thought of.

For example, let's say you want to find out if listening to music helps people focus better. Your experimental design would help you decide things like: Who are you going to test? What kind of music will you use? How will you measure focus? And, importantly, how will you make sure that it's really the music affecting focus and not something else, like the time of day or whether someone had a good breakfast?

In short, experimental design is the master plan that guides researchers through the process of collecting data, so they can answer questions in the most reliable way possible. It's like the GPS for the journey of discovery!

History of Experimental Design

Around 350 BCE, people like Aristotle were trying to figure out how the world works, but they mostly just thought really hard about things. They didn't test their ideas much. So while they were super smart, their methods weren't always the best for finding out the truth.

Fast forward to the Renaissance (14th to 17th centuries), a time of big changes and lots of curiosity. People like Galileo started to experiment by actually doing tests, like rolling balls down inclined planes to study motion. Galileo's work was cool because he combined thinking with doing. He'd have an idea, test it, look at the results, and then think some more. This approach was a lot more reliable than just sitting around and thinking.

Now, let's zoom ahead to the 18th and 19th centuries. This is when people like Francis Galton, an English polymath, started to get really systematic about experimentation. Galton was obsessed with measuring things. Seriously, he even tried to measure how good-looking people were ! His work helped create the foundations for a more organized approach to experiments.

Next stop: the early 20th century. Enter Ronald A. Fisher , a brilliant British statistician. Fisher was a game-changer. He came up with ideas that are like the bread and butter of modern experimental design.

Fisher invented the concept of the " control group "—that's a group of people or things that don't get the treatment you're testing, so you can compare them to those who do. He also stressed the importance of " randomization ," which means assigning people or things to different groups by chance, like drawing names out of a hat. This makes sure the experiment is fair and the results are trustworthy.

Around the same time, American psychologists like John B. Watson and B.F. Skinner were developing " behaviorism ." They focused on studying things that they could directly observe and measure, like actions and reactions.

Skinner even built boxes—called Skinner Boxes —to test how animals like pigeons and rats learn. Their work helped shape how psychologists design experiments today. Watson performed a very controversial experiment called The Little Albert experiment that helped describe behaviour through conditioning—in other words, how people learn to behave the way they do.

In the later part of the 20th century and into our time, computers have totally shaken things up. Researchers now use super powerful software to help design their experiments and crunch the numbers.

With computers, they can simulate complex experiments before they even start, which helps them predict what might happen. This is especially helpful in fields like medicine, where getting things right can be a matter of life and death.

Also, did you know that experimental designs aren't just for scientists in labs? They're used by people in all sorts of jobs, like marketing, education, and even video game design! Yes, someone probably ran an experiment to figure out what makes a game super fun to play.

So there you have it—a quick tour through the history of experimental design, from Aristotle's deep thoughts to Fisher's groundbreaking ideas, and all the way to today's computer-powered research. These designs are the recipes that help people from all walks of life find answers to their big questions.

Key Terms in Experimental Design

Before we dig into the different types of experimental designs, let's get comfy with some key terms. Understanding these terms will make it easier for us to explore the various types of experimental designs that researchers use to answer their big questions.

Independent Variable : This is what you change or control in your experiment to see what effect it has. Think of it as the "cause" in a cause-and-effect relationship. For example, if you're studying whether different types of music help people focus, the kind of music is the independent variable.

Dependent Variable : This is what you're measuring to see the effect of your independent variable. In our music and focus experiment, how well people focus is the dependent variable—it's what "depends" on the kind of music played.

Control Group : This is a group of people who don't get the special treatment or change you're testing. They help you see what happens when the independent variable is not applied. If you're testing whether a new medicine works, the control group would take a fake pill, called a placebo , instead of the real medicine.

Experimental Group : This is the group that gets the special treatment or change you're interested in. Going back to our medicine example, this group would get the actual medicine to see if it has any effect.

Randomization : This is like shaking things up in a fair way. You randomly put people into the control or experimental group so that each group is a good mix of different kinds of people. This helps make the results more reliable.

Sample : This is the group of people you're studying. They're a "sample" of a larger group that you're interested in. For instance, if you want to know how teenagers feel about a new video game, you might study a sample of 100 teenagers.

Bias : This is anything that might tilt your experiment one way or another without you realizing it. Like if you're testing a new kind of dog food and you only test it on poodles, that could create a bias because maybe poodles just really like that food and other breeds don't.

Data : This is the information you collect during the experiment. It's like the treasure you find on your journey of discovery!

Replication : This means doing the experiment more than once to make sure your findings hold up. It's like double-checking your answers on a test.

Hypothesis : This is your educated guess about what will happen in the experiment. It's like predicting the end of a movie based on the first half.

Steps of Experimental Design

Alright, let's say you're all fired up and ready to run your own experiment. Cool! But where do you start? Well, designing an experiment is a bit like planning a road trip. There are some key steps you've got to take to make sure you reach your destination. Let's break it down:

• Ask a Question : Before you hit the road, you've got to know where you're going. Same with experiments. You start with a question you want to answer, like "Does eating breakfast really make you do better in school?"
• Do Some Homework : Before you pack your bags, you look up the best places to visit, right? In science, this means reading up on what other people have already discovered about your topic.
• Form a Hypothesis : This is your educated guess about what you think will happen. It's like saying, "I bet this route will get us there faster."
• Plan the Details : Now you decide what kind of car you're driving (your experimental design), who's coming with you (your sample), and what snacks to bring (your variables).
• Randomization : Remember, this is like shuffling a deck of cards. You want to mix up who goes into your control and experimental groups to make sure it's a fair test.
• Run the Experiment : Finally, the rubber hits the road! You carry out your plan, making sure to collect your data carefully.
• Analyze the Data : Once the trip's over, you look at your photos and decide which ones are keepers. In science, this means looking at your data to see what it tells you.
• Draw Conclusions : Based on your data, did you find an answer to your question? This is like saying, "Yep, that route was faster," or "Nope, we hit a ton of traffic."
• Share Your Findings : After a great trip, you want to tell everyone about it, right? Scientists do the same by publishing their results so others can learn from them.
• Do It Again? : Sometimes one road trip just isn't enough. In the same way, scientists often repeat their experiments to make sure their findings are solid.

So there you have it! Those are the basic steps you need to follow when you're designing an experiment. Each step helps make sure that you're setting up a fair and reliable way to find answers to your big questions.

Let's get into examples of experimental designs.

1) True Experimental Design

In the world of experiments, the True Experimental Design is like the superstar quarterback everyone talks about. Born out of the early 20th-century work of statisticians like Ronald A. Fisher, this design is all about control, precision, and reliability.

Researchers carefully pick an independent variable to manipulate (remember, that's the thing they're changing on purpose) and measure the dependent variable (the effect they're studying). Then comes the magic trick—randomization. By randomly putting participants into either the control or experimental group, scientists make sure their experiment is as fair as possible.

No sneaky biases here!

True Experimental Design Pros

The pros of True Experimental Design are like the perks of a VIP ticket at a concert: you get the best and most trustworthy results. Because everything is controlled and randomized, you can feel pretty confident that the results aren't just a fluke.

True Experimental Design Cons

However, there's a catch. Sometimes, it's really tough to set up these experiments in a real-world situation. Imagine trying to control every single detail of your day, from the food you eat to the air you breathe. Not so easy, right?

True Experimental Design Uses

The fields that get the most out of True Experimental Designs are those that need super reliable results, like medical research.

When scientists were developing COVID-19 vaccines, they used this design to run clinical trials. They had control groups that received a placebo (a harmless substance with no effect) and experimental groups that got the actual vaccine. Then they measured how many people in each group got sick. By comparing the two, they could say, "Yep, this vaccine works!"

So next time you read about a groundbreaking discovery in medicine or technology, chances are a True Experimental Design was the VIP behind the scenes, making sure everything was on point. It's been the go-to for rigorous scientific inquiry for nearly a century, and it's not stepping off the stage anytime soon.

2) Quasi-Experimental Design

So, let's talk about the Quasi-Experimental Design. Think of this one as the cool cousin of True Experimental Design. It wants to be just like its famous relative, but it's a bit more laid-back and flexible. You'll find quasi-experimental designs when it's tricky to set up a full-blown True Experimental Design with all the bells and whistles.

Quasi-experiments still play with an independent variable, just like their stricter cousins. The big difference? They don't use randomization. It's like wanting to divide a bag of jelly beans equally between your friends, but you can't quite do it perfectly.

In real life, it's often not possible or ethical to randomly assign people to different groups, especially when dealing with sensitive topics like education or social issues. And that's where quasi-experiments come in.

Quasi-Experimental Design Pros

Even though they lack full randomization, quasi-experimental designs are like the Swiss Army knives of research: versatile and practical. They're especially popular in fields like education, sociology, and public policy.

For instance, when researchers wanted to figure out if the Head Start program , aimed at giving young kids a "head start" in school, was effective, they used a quasi-experimental design. They couldn't randomly assign kids to go or not go to preschool, but they could compare kids who did with kids who didn't.

Quasi-Experimental Design Cons

Of course, quasi-experiments come with their own bag of pros and cons. On the plus side, they're easier to set up and often cheaper than true experiments. But the flip side is that they're not as rock-solid in their conclusions. Because the groups aren't randomly assigned, there's always that little voice saying, "Hey, are we missing something here?"

Quasi-Experimental Design Uses

Quasi-Experimental Design gained traction in the mid-20th century. Researchers were grappling with real-world problems that didn't fit neatly into a laboratory setting. Plus, as society became more aware of ethical considerations, the need for flexible designs increased. So, the quasi-experimental approach was like a breath of fresh air for scientists wanting to study complex issues without a laundry list of restrictions.

In short, if True Experimental Design is the superstar quarterback, Quasi-Experimental Design is the versatile player who can adapt and still make significant contributions to the game.

3) Pre-Experimental Design

Now, let's talk about the Pre-Experimental Design. Imagine it as the beginner's skateboard you get before you try out for all the cool tricks. It has wheels, it rolls, but it's not built for the professional skatepark.

Similarly, pre-experimental designs give researchers a starting point. They let you dip your toes in the water of scientific research without diving in head-first.

So, what's the deal with pre-experimental designs?

Pre-Experimental Designs are the basic, no-frills versions of experiments. Researchers still mess around with an independent variable and measure a dependent variable, but they skip over the whole randomization thing and often don't even have a control group.

It's like baking a cake but forgetting the frosting and sprinkles; you'll get some results, but they might not be as complete or reliable as you'd like.

Pre-Experimental Design Pros

Why use such a simple setup? Because sometimes, you just need to get the ball rolling. Pre-experimental designs are great for quick-and-dirty research when you're short on time or resources. They give you a rough idea of what's happening, which you can use to plan more detailed studies later.

A good example of this is early studies on the effects of screen time on kids. Researchers couldn't control every aspect of a child's life, but they could easily ask parents to track how much time their kids spent in front of screens and then look for trends in behavior or school performance.

Pre-Experimental Design Cons

But here's the catch: pre-experimental designs are like that first draft of an essay. It helps you get your ideas down, but you wouldn't want to turn it in for a grade. Because these designs lack the rigorous structure of true or quasi-experimental setups, they can't give you rock-solid conclusions. They're more like clues or signposts pointing you in a certain direction.

Pre-Experimental Design Uses

This type of design became popular in the early stages of various scientific fields. Researchers used them to scratch the surface of a topic, generate some initial data, and then decide if it's worth exploring further. In other words, pre-experimental designs were the stepping stones that led to more complex, thorough investigations.

So, while Pre-Experimental Design may not be the star player on the team, it's like the practice squad that helps everyone get better. It's the starting point that can lead to bigger and better things.

4) Factorial Design

Now, buckle up, because we're moving into the world of Factorial Design, the multi-tasker of the experimental universe.

Imagine juggling not just one, but multiple balls in the air—that's what researchers do in a factorial design.

In Factorial Design, researchers are not satisfied with just studying one independent variable. Nope, they want to study two or more at the same time to see how they interact.

It's like cooking with several spices to see how they blend together to create unique flavors.

Factorial Design became the talk of the town with the rise of computers. Why? Because this design produces a lot of data, and computers are the number crunchers that help make sense of it all. So, thanks to our silicon friends, researchers can study complicated questions like, "How do diet AND exercise together affect weight loss?" instead of looking at just one of those factors.

Factorial Design Pros

This design's main selling point is its ability to explore interactions between variables. For instance, maybe a new study drug works really well for young people but not so great for older adults. A factorial design could reveal that age is a crucial factor, something you might miss if you only studied the drug's effectiveness in general. It's like being a detective who looks for clues not just in one room but throughout the entire house.

Factorial Design Cons

However, factorial designs have their own bag of challenges. First off, they can be pretty complicated to set up and run. Imagine coordinating a four-way intersection with lots of cars coming from all directions—you've got to make sure everything runs smoothly, or you'll end up with a traffic jam. Similarly, researchers need to carefully plan how they'll measure and analyze all the different variables.

Factorial Design Uses

Factorial designs are widely used in psychology to untangle the web of factors that influence human behavior. They're also popular in fields like marketing, where companies want to understand how different aspects like price, packaging, and advertising influence a product's success.

And speaking of success, the factorial design has been a hit since statisticians like Ronald A. Fisher (yep, him again!) expanded on it in the early-to-mid 20th century. It offered a more nuanced way of understanding the world, proving that sometimes, to get the full picture, you've got to juggle more than one ball at a time.

So, if True Experimental Design is the quarterback and Quasi-Experimental Design is the versatile player, Factorial Design is the strategist who sees the entire game board and makes moves accordingly.

5) Longitudinal Design

Alright, let's take a step into the world of Longitudinal Design. Picture it as the grand storyteller, the kind who doesn't just tell you about a single event but spins an epic tale that stretches over years or even decades. This design isn't about quick snapshots; it's about capturing the whole movie of someone's life or a long-running process.

You know how you might take a photo every year on your birthday to see how you've changed? Longitudinal Design is kind of like that, but for scientific research.

With Longitudinal Design, instead of measuring something just once, researchers come back again and again, sometimes over many years, to see how things are going. This helps them understand not just what's happening, but why it's happening and how it changes over time.

This design really started to shine in the latter half of the 20th century, when researchers began to realize that some questions can't be answered in a hurry. Think about studies that look at how kids grow up, or research on how a certain medicine affects you over a long period. These aren't things you can rush.

The famous Framingham Heart Study , started in 1948, is a prime example. It's been studying heart health in a small town in Massachusetts for decades, and the findings have shaped what we know about heart disease.

Longitudinal Design Pros

So, what's to love about Longitudinal Design? First off, it's the go-to for studying change over time, whether that's how people age or how a forest recovers from a fire.

Longitudinal Design Cons

But it's not all sunshine and rainbows. Longitudinal studies take a lot of patience and resources. Plus, keeping track of participants over many years can be like herding cats—difficult and full of surprises.

Longitudinal Design Uses

Despite these challenges, longitudinal studies have been key in fields like psychology, sociology, and medicine. They provide the kind of deep, long-term insights that other designs just can't match.

So, if the True Experimental Design is the superstar quarterback, and the Quasi-Experimental Design is the flexible athlete, then the Factorial Design is the strategist, and the Longitudinal Design is the wise elder who has seen it all and has stories to tell.

6) Cross-Sectional Design

Now, let's flip the script and talk about Cross-Sectional Design, the polar opposite of the Longitudinal Design. If Longitudinal is the grand storyteller, think of Cross-Sectional as the snapshot photographer. It captures a single moment in time, like a selfie that you take to remember a fun day. Researchers using this design collect all their data at one point, providing a kind of "snapshot" of whatever they're studying.

In a Cross-Sectional Design, researchers look at multiple groups all at the same time to see how they're different or similar.

This design rose to popularity in the mid-20th century, mainly because it's so quick and efficient. Imagine wanting to know how people of different ages feel about a new video game. Instead of waiting for years to see how opinions change, you could just ask people of all ages what they think right now. That's Cross-Sectional Design for you—fast and straightforward.

You'll find this type of research everywhere from marketing studies to healthcare. For instance, you might have heard about surveys asking people what they think about a new product or political issue. Those are usually cross-sectional studies, aimed at getting a quick read on public opinion.

Cross-Sectional Design Pros

So, what's the big deal with Cross-Sectional Design? Well, it's the go-to when you need answers fast and don't have the time or resources for a more complicated setup.

Cross-Sectional Design Cons

Remember, speed comes with trade-offs. While you get your results quickly, those results are stuck in time. They can't tell you how things change or why they're changing, just what's happening right now.

Cross-Sectional Design Uses

Also, because they're so quick and simple, cross-sectional studies often serve as the first step in research. They give scientists an idea of what's going on so they can decide if it's worth digging deeper. In that way, they're a bit like a movie trailer, giving you a taste of the action to see if you're interested in seeing the whole film.

So, in our lineup of experimental designs, if True Experimental Design is the superstar quarterback and Longitudinal Design is the wise elder, then Cross-Sectional Design is like the speedy running back—fast, agile, but not designed for long, drawn-out plays.

7) Correlational Design

Next on our roster is the Correlational Design, the keen observer of the experimental world. Imagine this design as the person at a party who loves people-watching. They don't interfere or get involved; they just observe and take mental notes about what's going on.

In a correlational study, researchers don't change or control anything; they simply observe and measure how two variables relate to each other.

The correlational design has roots in the early days of psychology and sociology. Pioneers like Sir Francis Galton used it to study how qualities like intelligence or height could be related within families.

This design is all about asking, "Hey, when this thing happens, does that other thing usually happen too?" For example, researchers might study whether students who have more study time get better grades or whether people who exercise more have lower stress levels.

One of the most famous correlational studies you might have heard of is the link between smoking and lung cancer. Back in the mid-20th century, researchers started noticing that people who smoked a lot also seemed to get lung cancer more often. They couldn't say smoking caused cancer—that would require a true experiment—but the strong correlation was a red flag that led to more research and eventually, health warnings.

Correlational Design Pros

This design is great at proving that two (or more) things can be related. Correlational designs can help prove that more detailed research is needed on a topic. They can help us see patterns or possible causes for things that we otherwise might not have realized.

Correlational Design Cons

But here's where you need to be careful: correlational designs can be tricky. Just because two things are related doesn't mean one causes the other. That's like saying, "Every time I wear my lucky socks, my team wins." Well, it's a fun thought, but those socks aren't really controlling the game.

Correlational Design Uses

Despite this limitation, correlational designs are popular in psychology, economics, and epidemiology, to name a few fields. They're often the first step in exploring a possible relationship between variables. Once a strong correlation is found, researchers may decide to conduct more rigorous experimental studies to examine cause and effect.

So, if the True Experimental Design is the superstar quarterback and the Longitudinal Design is the wise elder, the Factorial Design is the strategist, and the Cross-Sectional Design is the speedster, then the Correlational Design is the clever scout, identifying interesting patterns but leaving the heavy lifting of proving cause and effect to the other types of designs.

8) Meta-Analysis

Last but not least, let's talk about Meta-Analysis, the librarian of experimental designs.

If other designs are all about creating new research, Meta-Analysis is about gathering up everyone else's research, sorting it, and figuring out what it all means when you put it together.

Imagine a jigsaw puzzle where each piece is a different study. Meta-Analysis is the process of fitting all those pieces together to see the big picture.

The concept of Meta-Analysis started to take shape in the late 20th century, when computers became powerful enough to handle massive amounts of data. It was like someone handed researchers a super-powered magnifying glass, letting them examine multiple studies at the same time to find common trends or results.

You might have heard of the Cochrane Reviews in healthcare . These are big collections of meta-analyses that help doctors and policymakers figure out what treatments work best based on all the research that's been done.

For example, if ten different studies show that a certain medicine helps lower blood pressure, a meta-analysis would pull all that information together to give a more accurate answer.

Meta-Analysis Pros

The beauty of Meta-Analysis is that it can provide really strong evidence. Instead of relying on one study, you're looking at the whole landscape of research on a topic.

Meta-Analysis Cons

However, it does have some downsides. For one, Meta-Analysis is only as good as the studies it includes. If those studies are flawed, the meta-analysis will be too. It's like baking a cake: if you use bad ingredients, it doesn't matter how good your recipe is—the cake won't turn out well.

Meta-Analysis Uses

Despite these challenges, meta-analyses are highly respected and widely used in many fields like medicine, psychology, and education. They help us make sense of a world that's bursting with information by showing us the big picture drawn from many smaller snapshots.

So, in our all-star lineup, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, the Factorial Design is the strategist, the Cross-Sectional Design is the speedster, and the Correlational Design is the scout, then the Meta-Analysis is like the coach, using insights from everyone else's plays to come up with the best game plan.

9) Non-Experimental Design

Now, let's talk about a player who's a bit of an outsider on this team of experimental designs—the Non-Experimental Design. Think of this design as the commentator or the journalist who covers the game but doesn't actually play.

In a Non-Experimental Design, researchers are like reporters gathering facts, but they don't interfere or change anything. They're simply there to describe and analyze.

Non-Experimental Design Pros

So, what's the deal with Non-Experimental Design? Its strength is in description and exploration. It's really good for studying things as they are in the real world, without changing any conditions.

Non-Experimental Design Cons

Because a non-experimental design doesn't manipulate variables, it can't prove cause and effect. It's like a weather reporter: they can tell you it's raining, but they can't tell you why it's raining.

The downside? Since researchers aren't controlling variables, it's hard to rule out other explanations for what they observe. It's like hearing one side of a story—you get an idea of what happened, but it might not be the complete picture.

Non-Experimental Design Uses

Non-Experimental Design has always been a part of research, especially in fields like anthropology, sociology, and some areas of psychology.

For instance, if you've ever heard of studies that describe how people behave in different cultures or what teens like to do in their free time, that's often Non-Experimental Design at work. These studies aim to capture the essence of a situation, like painting a portrait instead of taking a snapshot.

One well-known example you might have heard about is the Kinsey Reports from the 1940s and 1950s, which described sexual behavior in men and women. Researchers interviewed thousands of people but didn't manipulate any variables like you would in a true experiment. They simply collected data to create a comprehensive picture of the subject matter.

So, in our metaphorical team of research designs, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, and Meta-Analysis is the coach, then Non-Experimental Design is the sports journalist—always present, capturing the game, but not part of the action itself.

10) Repeated Measures Design

Time to meet the Repeated Measures Design, the time traveler of our research team. If this design were a player in a sports game, it would be the one who keeps revisiting past plays to figure out how to improve the next one.

Repeated Measures Design is all about studying the same people or subjects multiple times to see how they change or react under different conditions.

The idea behind Repeated Measures Design isn't new; it's been around since the early days of psychology and medicine. You could say it's a cousin to the Longitudinal Design, but instead of looking at how things naturally change over time, it focuses on how the same group reacts to different things.

Imagine a study looking at how a new energy drink affects people's running speed. Instead of comparing one group that drank the energy drink to another group that didn't, a Repeated Measures Design would have the same group of people run multiple times—once with the energy drink, and once without. This way, you're really zeroing in on the effect of that energy drink, making the results more reliable.

Repeated Measures Design Pros

The strong point of Repeated Measures Design is that it's super focused. Because it uses the same subjects, you don't have to worry about differences between groups messing up your results.

Repeated Measures Design Cons

But the downside? Well, people can get tired or bored if they're tested too many times, which might affect how they respond.

Repeated Measures Design Uses

A famous example of this design is the "Little Albert" experiment, conducted by John B. Watson and Rosalie Rayner in 1920. In this study, a young boy was exposed to a white rat and other stimuli several times to see how his emotional responses changed. Though the ethical standards of this experiment are often criticized today, it was groundbreaking in understanding conditioned emotional responses.

In our metaphorical lineup of research designs, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, and Non-Experimental Design is the journalist, then Repeated Measures Design is the time traveler—always looping back to fine-tune the game plan.

11) Crossover Design

Next up is Crossover Design, the switch-hitter of the research world. If you're familiar with baseball, you'll know a switch-hitter is someone who can bat both right-handed and left-handed.

In a similar way, Crossover Design allows subjects to experience multiple conditions, flipping them around so that everyone gets a turn in each role.

This design is like the utility player on our team—versatile, flexible, and really good at adapting.

The Crossover Design has its roots in medical research and has been popular since the mid-20th century. It's often used in clinical trials to test the effectiveness of different treatments.

Crossover Design Pros

The neat thing about this design is that it allows each participant to serve as their own control group. Imagine you're testing two new kinds of headache medicine. Instead of giving one type to one group and another type to a different group, you'd give both kinds to the same people but at different times.

Crossover Design Cons

What's the big deal with Crossover Design? Its major strength is in reducing the "noise" that comes from individual differences. Since each person experiences all conditions, it's easier to see real effects. However, there's a catch. This design assumes that there's no lasting effect from the first condition when you switch to the second one. That might not always be true. If the first treatment has a long-lasting effect, it could mess up the results when you switch to the second treatment.

Crossover Design Uses

A well-known example of Crossover Design is in studies that look at the effects of different types of diets—like low-carb vs. low-fat diets. Researchers might have participants follow a low-carb diet for a few weeks, then switch them to a low-fat diet. By doing this, they can more accurately measure how each diet affects the same group of people.

In our team of experimental designs, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, and Repeated Measures Design is the time traveler, then Crossover Design is the versatile utility player—always ready to adapt and play multiple roles to get the most accurate results.

12) Cluster Randomized Design

Meet the Cluster Randomized Design, the team captain of group-focused research. In our imaginary lineup of experimental designs, if other designs focus on individual players, then Cluster Randomized Design is looking at how the entire team functions.

This approach is especially common in educational and community-based research, and it's been gaining traction since the late 20th century.

Here's how Cluster Randomized Design works: Instead of assigning individual people to different conditions, researchers assign entire groups, or "clusters." These could be schools, neighborhoods, or even entire towns. This helps you see how the new method works in a real-world setting.

Imagine you want to see if a new anti-bullying program really works. Instead of selecting individual students, you'd introduce the program to a whole school or maybe even several schools, and then compare the results to schools without the program.

Cluster Randomized Design Pros

Why use Cluster Randomized Design? Well, sometimes it's just not practical to assign conditions at the individual level. For example, you can't really have half a school following a new reading program while the other half sticks with the old one; that would be way too confusing! Cluster Randomization helps get around this problem by treating each "cluster" as its own mini-experiment.

Cluster Randomized Design Cons

There's a downside, too. Because entire groups are assigned to each condition, there's a risk that the groups might be different in some important way that the researchers didn't account for. That's like having one sports team that's full of veterans playing against a team of rookies; the match wouldn't be fair.

Cluster Randomized Design Uses

A famous example is the research conducted to test the effectiveness of different public health interventions, like vaccination programs. Researchers might roll out a vaccination program in one community but not in another, then compare the rates of disease in both.

In our metaphorical research team, if True Experimental Design is the quarterback, Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, Repeated Measures Design is the time traveler, and Crossover Design is the utility player, then Cluster Randomized Design is the team captain—always looking out for the group as a whole.

13) Mixed-Methods Design

Say hello to Mixed-Methods Design, the all-rounder or the "Renaissance player" of our research team.

Mixed-Methods Design uses a blend of both qualitative and quantitative methods to get a more complete picture, just like a Renaissance person who's good at lots of different things. It's like being good at both offense and defense in a sport; you've got all your bases covered!

Mixed-Methods Design is a fairly new kid on the block, becoming more popular in the late 20th and early 21st centuries as researchers began to see the value in using multiple approaches to tackle complex questions. It's the Swiss Army knife in our research toolkit, combining the best parts of other designs to be more versatile.

Here's how it could work: Imagine you're studying the effects of a new educational app on students' math skills. You might use quantitative methods like tests and grades to measure how much the students improve—that's the 'numbers part.'

But you also want to know how the students feel about math now, or why they think they got better or worse. For that, you could conduct interviews or have students fill out journals—that's the 'story part.'

Mixed-Methods Design Pros

So, what's the scoop on Mixed-Methods Design? The strength is its versatility and depth; you're not just getting numbers or stories, you're getting both, which gives a fuller picture.

Mixed-Methods Design Cons

But, it's also more challenging. Imagine trying to play two sports at the same time! You have to be skilled in different research methods and know how to combine them effectively.

Mixed-Methods Design Uses

A high-profile example of Mixed-Methods Design is research on climate change. Scientists use numbers and data to show temperature changes (quantitative), but they also interview people to understand how these changes are affecting communities (qualitative).

In our team of experimental designs, if True Experimental Design is the quarterback, Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, Repeated Measures Design is the time traveler, Crossover Design is the utility player, and Cluster Randomized Design is the team captain, then Mixed-Methods Design is the Renaissance player—skilled in multiple areas and able to bring them all together for a winning strategy.

14) Multivariate Design

Now, let's turn our attention to Multivariate Design, the multitasker of the research world.

If our lineup of research designs were like players on a basketball court, Multivariate Design would be the player dribbling, passing, and shooting all at once. This design doesn't just look at one or two things; it looks at several variables simultaneously to see how they interact and affect each other.

Multivariate Design is like baking a cake with many ingredients. Instead of just looking at how flour affects the cake, you also consider sugar, eggs, and milk all at once. This way, you understand how everything works together to make the cake taste good or bad.

Multivariate Design has been a go-to method in psychology, economics, and social sciences since the latter half of the 20th century. With the advent of computers and advanced statistical software, analyzing multiple variables at once became a lot easier, and Multivariate Design soared in popularity.

Multivariate Design Pros

So, what's the benefit of using Multivariate Design? Its power lies in its complexity. By studying multiple variables at the same time, you can get a really rich, detailed understanding of what's going on.

Multivariate Design Cons

But that complexity can also be a drawback. With so many variables, it can be tough to tell which ones are really making a difference and which ones are just along for the ride.

Multivariate Design Uses

Imagine you're a coach trying to figure out the best strategy to win games. You wouldn't just look at how many points your star player scores; you'd also consider assists, rebounds, turnovers, and maybe even how loud the crowd is. A Multivariate Design would help you understand how all these factors work together to determine whether you win or lose.

A well-known example of Multivariate Design is in market research. Companies often use this approach to figure out how different factors—like price, packaging, and advertising—affect sales. By studying multiple variables at once, they can find the best combination to boost profits.

In our metaphorical research team, if True Experimental Design is the quarterback, Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, Repeated Measures Design is the time traveler, Crossover Design is the utility player, Cluster Randomized Design is the team captain, and Mixed-Methods Design is the Renaissance player, then Multivariate Design is the multitasker—juggling many variables at once to get a fuller picture of what's happening.

15) Pretest-Posttest Design

Let's introduce Pretest-Posttest Design, the "Before and After" superstar of our research team. You've probably seen those before-and-after pictures in ads for weight loss programs or home renovations, right?

Well, this design is like that, but for science! Pretest-Posttest Design checks out what things are like before the experiment starts and then compares that to what things are like after the experiment ends.

This design is one of the classics, a staple in research for decades across various fields like psychology, education, and healthcare. It's so simple and straightforward that it has stayed popular for a long time.

In Pretest-Posttest Design, you measure your subject's behavior or condition before you introduce any changes—that's your "before" or "pretest." Then you do your experiment, and after it's done, you measure the same thing again—that's your "after" or "posttest."

Pretest-Posttest Design Pros

What makes Pretest-Posttest Design special? It's pretty easy to understand and doesn't require fancy statistics.

Pretest-Posttest Design Cons

But there are some pitfalls. For example, what if the kids in our math example get better at multiplication just because they're older or because they've taken the test before? That would make it hard to tell if the program is really effective or not.

Pretest-Posttest Design Uses

Let's say you're a teacher and you want to know if a new math program helps kids get better at multiplication. First, you'd give all the kids a multiplication test—that's your pretest. Then you'd teach them using the new math program. At the end, you'd give them the same test again—that's your posttest. If the kids do better on the second test, you might conclude that the program works.

One famous use of Pretest-Posttest Design is in evaluating the effectiveness of driver's education courses. Researchers will measure people's driving skills before and after the course to see if they've improved.

16) Solomon Four-Group Design

Next up is the Solomon Four-Group Design, the "chess master" of our research team. This design is all about strategy and careful planning. Named after Richard L. Solomon who introduced it in the 1940s, this method tries to correct some of the weaknesses in simpler designs, like the Pretest-Posttest Design.

Here's how it rolls: The Solomon Four-Group Design uses four different groups to test a hypothesis. Two groups get a pretest, then one of them receives the treatment or intervention, and both get a posttest. The other two groups skip the pretest, and only one of them receives the treatment before they both get a posttest.

Sound complicated? It's like playing 4D chess; you're thinking several moves ahead!

Solomon Four-Group Design Pros

What's the pro and con of the Solomon Four-Group Design? On the plus side, it provides really robust results because it accounts for so many variables.

Solomon Four-Group Design Cons

The downside? It's a lot of work and requires a lot of participants, making it more time-consuming and costly.

Solomon Four-Group Design Uses

Let's say you want to figure out if a new way of teaching history helps students remember facts better. Two classes take a history quiz (pretest), then one class uses the new teaching method while the other sticks with the old way. Both classes take another quiz afterward (posttest).

Meanwhile, two more classes skip the initial quiz, and then one uses the new method before both take the final quiz. Comparing all four groups will give you a much clearer picture of whether the new teaching method works and whether the pretest itself affects the outcome.

The Solomon Four-Group Design is less commonly used than simpler designs but is highly respected for its ability to control for more variables. It's a favorite in educational and psychological research where you really want to dig deep and figure out what's actually causing changes.

17) Adaptive Designs

Now, let's talk about Adaptive Designs, the chameleons of the experimental world.

Imagine you're a detective, and halfway through solving a case, you find a clue that changes everything. You wouldn't just stick to your old plan; you'd adapt and change your approach, right? That's exactly what Adaptive Designs allow researchers to do.

In an Adaptive Design, researchers can make changes to the study as it's happening, based on early results. In a traditional study, once you set your plan, you stick to it from start to finish.

Adaptive Design Pros

This method is particularly useful in fast-paced or high-stakes situations, like developing a new vaccine in the middle of a pandemic. The ability to adapt can save both time and resources, and more importantly, it can save lives by getting effective treatments out faster.

Adaptive Design Cons

But Adaptive Designs aren't without their drawbacks. They can be very complex to plan and carry out, and there's always a risk that the changes made during the study could introduce bias or errors.

Adaptive Design Uses

Adaptive Designs are most often seen in clinical trials, particularly in the medical and pharmaceutical fields.

For instance, if a new drug is showing really promising results, the study might be adjusted to give more participants the new treatment instead of a placebo. Or if one dose level is showing bad side effects, it might be dropped from the study.

The best part is, these changes are pre-planned. Researchers lay out in advance what changes might be made and under what conditions, which helps keep everything scientific and above board.

In terms of applications, besides their heavy usage in medical and pharmaceutical research, Adaptive Designs are also becoming increasingly popular in software testing and market research. In these fields, being able to quickly adjust to early results can give companies a significant advantage.

Adaptive Designs are like the agile startups of the research world—quick to pivot, keen to learn from ongoing results, and focused on rapid, efficient progress. However, they require a great deal of expertise and careful planning to ensure that the adaptability doesn't compromise the integrity of the research.

18) Bayesian Designs

Next, let's dive into Bayesian Designs, the data detectives of the research universe. Named after Thomas Bayes, an 18th-century statistician and minister, this design doesn't just look at what's happening now; it also takes into account what's happened before.

Imagine if you were a detective who not only looked at the evidence in front of you but also used your past cases to make better guesses about your current one. That's the essence of Bayesian Designs.

Bayesian Designs are like detective work in science. As you gather more clues (or data), you update your best guess on what's really happening. This way, your experiment gets smarter as it goes along.

In the world of research, Bayesian Designs are most notably used in areas where you have some prior knowledge that can inform your current study. For example, if earlier research shows that a certain type of medicine usually works well for a specific illness, a Bayesian Design would include that information when studying a new group of patients with the same illness.

Bayesian Design Pros

One of the major advantages of Bayesian Designs is their efficiency. Because they use existing data to inform the current experiment, often fewer resources are needed to reach a reliable conclusion.

Bayesian Design Cons

However, they can be quite complicated to set up and require a deep understanding of both statistics and the subject matter at hand.

Bayesian Design Uses

Bayesian Designs are highly valued in medical research, finance, environmental science, and even in Internet search algorithms. Their ability to continually update and refine hypotheses based on new evidence makes them particularly useful in fields where data is constantly evolving and where quick, informed decisions are crucial.

Here's a real-world example: In the development of personalized medicine, where treatments are tailored to individual patients, Bayesian Designs are invaluable. If a treatment has been effective for patients with similar genetics or symptoms in the past, a Bayesian approach can use that data to predict how well it might work for a new patient.

This type of design is also increasingly popular in machine learning and artificial intelligence. In these fields, Bayesian Designs help algorithms "learn" from past data to make better predictions or decisions in new situations. It's like teaching a computer to be a detective that gets better and better at solving puzzles the more puzzles it sees.

19) Covariate Adaptive Randomization

Now let's turn our attention to Covariate Adaptive Randomization, which you can think of as the "matchmaker" of experimental designs.

Picture a soccer coach trying to create the most balanced teams for a friendly match. They wouldn't just randomly assign players; they'd take into account each player's skills, experience, and other traits.

Covariate Adaptive Randomization is all about creating the most evenly matched groups possible for an experiment.

In traditional randomization, participants are allocated to different groups purely by chance. This is a pretty fair way to do things, but it can sometimes lead to unbalanced groups.

Imagine if all the professional-level players ended up on one soccer team and all the beginners on another; that wouldn't be a very informative match! Covariate Adaptive Randomization fixes this by using important traits or characteristics (called "covariates") to guide the randomization process.

Covariate Adaptive Randomization Pros

The benefits of this design are pretty clear: it aims for balance and fairness, making the final results more trustworthy.

Covariate Adaptive Randomization Cons

But it's not perfect. It can be complex to implement and requires a deep understanding of which characteristics are most important to balance.

Covariate Adaptive Randomization Uses

This design is particularly useful in medical trials. Let's say researchers are testing a new medication for high blood pressure. Participants might have different ages, weights, or pre-existing conditions that could affect the results.

Covariate Adaptive Randomization would make sure that each treatment group has a similar mix of these characteristics, making the results more reliable and easier to interpret.

In practical terms, this design is often seen in clinical trials for new drugs or therapies, but its principles are also applicable in fields like psychology, education, and social sciences.

For instance, in educational research, it might be used to ensure that classrooms being compared have similar distributions of students in terms of academic ability, socioeconomic status, and other factors.

Covariate Adaptive Randomization is like the wise elder of the group, ensuring that everyone has an equal opportunity to show their true capabilities, thereby making the collective results as reliable as possible.

20) Stepped Wedge Design

Let's now focus on the Stepped Wedge Design, a thoughtful and cautious member of the experimental design family.

Imagine you're trying out a new gardening technique, but you're not sure how well it will work. You decide to apply it to one section of your garden first, watch how it performs, and then gradually extend the technique to other sections. This way, you get to see its effects over time and across different conditions. That's basically how Stepped Wedge Design works.

In a Stepped Wedge Design, all participants or clusters start off in the control group, and then, at different times, they 'step' over to the intervention or treatment group. This creates a wedge-like pattern over time where more and more participants receive the treatment as the study progresses. It's like rolling out a new policy in phases, monitoring its impact at each stage before extending it to more people.

Stepped Wedge Design Pros

The Stepped Wedge Design offers several advantages. Firstly, it allows for the study of interventions that are expected to do more good than harm, which makes it ethically appealing.

Secondly, it's useful when resources are limited and it's not feasible to roll out a new treatment to everyone at once. Lastly, because everyone eventually receives the treatment, it can be easier to get buy-in from participants or organizations involved in the study.

Stepped Wedge Design Cons

However, this design can be complex to analyze because it has to account for both the time factor and the changing conditions in each 'step' of the wedge. And like any study where participants know they're receiving an intervention, there's the potential for the results to be influenced by the placebo effect or other biases.

Stepped Wedge Design Uses

This design is particularly useful in health and social care research. For instance, if a hospital wants to implement a new hygiene protocol, it might start in one department, assess its impact, and then roll it out to other departments over time. This allows the hospital to adjust and refine the new protocol based on real-world data before it's fully implemented.

In terms of applications, Stepped Wedge Designs are commonly used in public health initiatives, organizational changes in healthcare settings, and social policy trials. They are particularly useful in situations where an intervention is being rolled out gradually and it's important to understand its impacts at each stage.

21) Sequential Design

Next up is Sequential Design, the dynamic and flexible member of our experimental design family.

Imagine you're playing a video game where you can choose different paths. If you take one path and find a treasure chest, you might decide to continue in that direction. If you hit a dead end, you might backtrack and try a different route. Sequential Design operates in a similar fashion, allowing researchers to make decisions at different stages based on what they've learned so far.

In a Sequential Design, the experiment is broken down into smaller parts, or "sequences." After each sequence, researchers pause to look at the data they've collected. Based on those findings, they then decide whether to stop the experiment because they've got enough information, or to continue and perhaps even modify the next sequence.

Sequential Design Pros

This allows for a more efficient use of resources, as you're only continuing with the experiment if the data suggests it's worth doing so.

One of the great things about Sequential Design is its efficiency. Because you're making data-driven decisions along the way, you can often reach conclusions more quickly and with fewer resources.

Sequential Design Cons

However, it requires careful planning and expertise to ensure that these "stop or go" decisions are made correctly and without bias.

Sequential Design Uses

In terms of its applications, besides healthcare and medicine, Sequential Design is also popular in quality control in manufacturing, environmental monitoring, and financial modeling. In these areas, being able to make quick decisions based on incoming data can be a big advantage.

This design is often used in clinical trials involving new medications or treatments. For example, if early results show that a new drug has significant side effects, the trial can be stopped before more people are exposed to it.

On the flip side, if the drug is showing promising results, the trial might be expanded to include more participants or to extend the testing period.

Think of Sequential Design as the nimble athlete of experimental designs, capable of quick pivots and adjustments to reach the finish line in the most effective way possible. But just like an athlete needs a good coach, this design requires expert oversight to make sure it stays on the right track.

22) Field Experiments

Last but certainly not least, let's explore Field Experiments—the adventurers of the experimental design world.

Picture a scientist leaving the controlled environment of a lab to test a theory in the real world, like a biologist studying animals in their natural habitat or a social scientist observing people in a real community. These are Field Experiments, and they're all about getting out there and gathering data in real-world settings.

Field Experiments embrace the messiness of the real world, unlike laboratory experiments, where everything is controlled down to the smallest detail. This makes them both exciting and challenging.

Field Experiment Pros

On one hand, the results often give us a better understanding of how things work outside the lab.

While Field Experiments offer real-world relevance, they come with challenges like controlling for outside factors and the ethical considerations of intervening in people's lives without their knowledge.

Field Experiment Cons

On the other hand, the lack of control can make it harder to tell exactly what's causing what. Yet, despite these challenges, they remain a valuable tool for researchers who want to understand how theories play out in the real world.

Field Experiment Uses

Let's say a school wants to improve student performance. In a Field Experiment, they might change the school's daily schedule for one semester and keep track of how students perform compared to another school where the schedule remained the same.

Because the study is happening in a real school with real students, the results could be very useful for understanding how the change might work in other schools. But since it's the real world, lots of other factors—like changes in teachers or even the weather—could affect the results.

Field Experiments are widely used in economics, psychology, education, and public policy. For example, you might have heard of the famous "Broken Windows" experiment in the 1980s that looked at how small signs of disorder, like broken windows or graffiti, could encourage more serious crime in neighborhoods. This experiment had a big impact on how cities think about crime prevention.

From the foundational concepts of control groups and independent variables to the sophisticated layouts like Covariate Adaptive Randomization and Sequential Design, it's clear that the realm of experimental design is as varied as it is fascinating.

We've seen that each design has its own special talents, ideal for specific situations. Some designs, like the Classic Controlled Experiment, are like reliable old friends you can always count on.

Others, like Sequential Design, are flexible and adaptable, making quick changes based on what they learn. And let's not forget the adventurous Field Experiments, which take us out of the lab and into the real world to discover things we might not see otherwise.

Choosing the right experimental design is like picking the right tool for the job. The method you choose can make a big difference in how reliable your results are and how much people will trust what you've discovered. And as we've learned, there's a design to suit just about every question, every problem, and every curiosity.

So the next time you read about a new discovery in medicine, psychology, or any other field, you'll have a better understanding of the thought and planning that went into figuring things out. Experimental design is more than just a set of rules; it's a structured way to explore the unknown and answer questions that can change the world.

Related posts:

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• The Little Albert Experiment
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Exploring the Art of Experimental Design: A Step-by-Step Guide for Students and Educators

Experimental design for students.

Experimental design is a key method used in subjects like biology, chemistry, physics, psychology, and social sciences. It helps us figure out how different factors affect what we're studying, whether it's plants, chemicals, physical laws, human behavior, or how society works. Basically, it's a way to set up experiments so we can test ideas, see what happens, and make sense of our results. It's super important for students and researchers who want to answer big questions in science and understand the world better. Experimental design skills can be applied in situations ranging from problem solving to data analysis; they are wide reaching and can frequently be applied outside the classroom. The teaching of these skills is a very important part of science education, but is often overlooked when focused on teaching the content. As science educators, we have all seen the benefits practical work has for student engagement and understanding. However, with the time constraints placed on the curriculum, the time needed for students to develop these experimental research design and investigative skills can get squeezed out. Too often they get a ‘recipe’ to follow, which doesn’t allow them to take ownership of their practical work. From a very young age, they start to think about the world around them. They ask questions then use observations and evidence to answer them. Students tend to have intelligent, interesting, and testable questions that they love to ask. As educators, we should be working towards encouraging these questions and in turn, nurturing this natural curiosity in the world around them.

Teaching the design of experiments and letting students develop their own questions and hypotheses takes time. These materials have been created to scaffold and structure the process to allow teachers to focus on improving the key ideas in experimental design. Allowing students to ask their own questions, write their own hypotheses, and plan and carry out their own investigations is a valuable experience for them. This will lead to students having more ownership of their work. When students carry out the experimental method for their own questions, they reflect on how scientists have historically come to understand how the universe works.

Take a look at the printer-friendly pages and worksheet templates below!

What are the Steps of Experimental Design?

Embarking on the journey of scientific discovery begins with mastering experimental design steps. This foundational process is essential for formulating experiments that yield reliable and insightful results, guiding researchers and students alike through the detailed planning, experimental research design, and execution of their studies. By leveraging an experimental design template, participants can ensure the integrity and validity of their findings. Whether it's through designing a scientific experiment or engaging in experimental design activities, the aim is to foster a deep understanding of the fundamentals: How should experiments be designed? What are the 7 experimental design steps? How can you design your own experiment?

This is an exploration of the seven key experimental method steps, experimental design ideas, and ways to integrate design of experiments. Student projects can benefit greatly from supplemental worksheets and we will also provide resources such as worksheets aimed at teaching experimental design effectively. Let’s dive into the essential stages that underpin the process of designing an experiment, equipping learners with the tools to explore their scientific curiosity.

1. Question

This is a key part of the scientific method and the experimental design process. Students enjoy coming up with questions. Formulating questions is a deep and meaningful activity that can give students ownership over their work. A great way of getting students to think of how to visualize their research question is using a mind map storyboard.

Ask students to think of any questions they want to answer about the universe or get them to think about questions they have about a particular topic. All questions are good questions, but some are easier to test than others.

2. Hypothesis

A hypothesis is known as an educated guess. A hypothesis should be a statement that can be tested scientifically. At the end of the experiment, look back to see whether the conclusion supports the hypothesis or not.

Forming good hypotheses can be challenging for students to grasp. It is important to remember that the hypothesis is not a research question, it is a testable statement . One way of forming a hypothesis is to form it as an “if... then...” statement. This certainly isn't the only or best way to form a hypothesis, but can be a very easy formula for students to use when first starting out.

An “if... then...” statement requires students to identify the variables first, and that may change the order in which they complete the stages of the visual organizer. After identifying the dependent and independent variables, the hypothesis then takes the form if [change in independent variable], then [change in dependent variable].

For example, if an experiment were looking for the effect of caffeine on reaction time, the independent variable would be amount of caffeine and the dependent variable would be reaction time. The “if, then” hypothesis could be: If you increase the amount of caffeine taken, then the reaction time will decrease.

3. Explanation of Hypothesis

What led you to this hypothesis? What is the scientific background behind your hypothesis? Depending on age and ability, students use their prior knowledge to explain why they have chosen their hypotheses, or alternatively, research using books or the internet. This could also be a good time to discuss with students what a reliable source is.

For example, students may reference previous studies showing the alertness effects of caffeine to explain why they hypothesize caffeine intake will reduce reaction time.

4. Prediction

The prediction is slightly different to the hypothesis. A hypothesis is a testable statement, whereas the prediction is more specific to the experiment. In the discovery of the structure of DNA, the hypothesis proposed that DNA has a helical structure. The prediction was that the X-ray diffraction pattern of DNA would be an X shape.

Students should formulate a prediction that is a specific, measurable outcome based on their hypothesis. Rather than just stating "caffeine will decrease reaction time," students could predict that "drinking 2 cans of soda (90mg caffeine) will reduce average reaction time by 50 milliseconds compared to drinking no caffeine."

5. Identification of Variables

Below is an example of a Discussion Storyboard that can be used to get your students talking about variables in experimental design.

The three types of variables you will need to discuss with your students are dependent, independent, and controlled variables. To keep this simple, refer to these as "what you are going to measure", "what you are going to change", and "what you are going to keep the same". With more advanced students, you should encourage them to use the correct vocabulary.

Dependent variables are what is measured or observed by the scientist. These measurements will often be repeated because repeated measurements makes your data more reliable.

The independent variables are variables that scientists decide to change to see what effect it has on the dependent variable. Only one is chosen because it would be difficult to figure out which variable is causing any change you observe.

Controlled variables are quantities or factors that scientists want to remain the same throughout the experiment. They are controlled to remain constant, so as to not affect the dependent variable. Controlling these allows scientists to see how the independent variable affects the dependent variable within the experimental group.

Use this example below in your lessons, or delete the answers and set it as an activity for students to complete on Storyboard That.

6. Risk Assessment

Ultimately this must be signed off on by a responsible adult, but it is important to get students to think about how they will keep themselves safe. In this part, students should identify potential risks and then explain how they are going to minimize risk. An activity to help students develop these skills is to get them to identify and manage risks in different situations. Using the storyboard below, get students to complete the second column of the T-chart by saying, "What is risk?", then explaining how they could manage that risk. This storyboard could also be projected for a class discussion.

7. Materials

In this section, students will list the materials they need for the experiments, including any safety equipment that they have highlighted as needing in the risk assessment section. This is a great time to talk to students about choosing tools that are suitable for the job. You are going to use a different tool to measure the width of a hair than to measure the width of a football field!

8. General Plan and Diagram

It is important to talk to students about reproducibility. They should write a procedure that would allow their experimental method to be reproduced easily by another scientist. The easiest and most concise way for students to do this is by making a numbered list of instructions. A useful activity here could be getting students to explain how to make a cup of tea or a sandwich. Act out the process, pointing out any steps they’ve missed.

For English Language Learners and students who struggle with written English, students can describe the steps in their experiment visually using Storyboard That.

Not every experiment will need a diagram, but some plans will be greatly improved by including one. Have students focus on producing clear and easy-to-understand diagrams that illustrate the experimental group.

For example, a procedure to test the effect of sunlight on plant growth utilizing completely randomized design could detail:

• Select 10 similar seedlings of the same age and variety
• Prepare 2 identical trays with the same soil mixture
• Place 5 plants in each tray; label one set "sunlight" and one set "shade"
• Position sunlight tray by a south-facing window, and shade tray in a dark closet
• Water both trays with 50 mL water every 2 days
• After 3 weeks, remove plants and measure heights in cm

9. Carry Out Experiment

Once their procedure is approved, students should carefully carry out their planned experiment, following their written instructions. As data is collected, students should organize the raw results in tables, graphs, photos or drawings. This creates clear documentation for analyzing trends.

Some best practices for data collection include:

• Record quantitative data numerically with units
• Note qualitative observations with detailed descriptions
• Capture set up through illustrations or photos
• Write observations of unexpected events
• Identify data outliers and sources of error

For example, in the plant growth experiment, students could record:

They would also describe observations like leaf color change or directional bending visually or in writing.

It is crucial that students practice safe science procedures. Adult supervision is required for experimentation, along with proper risk assessment.

Well-documented data collection allows for deeper analysis after experiment completion to determine whether hypotheses and predictions were supported.

Completed Examples

Resources and Experimental Design Examples

Using visual organizers is an effective way to get your students working as scientists in the classroom.

There are many ways to use these investigation planning tools to scaffold and structure students' work while they are working as scientists. Students can complete the planning stage on Storyboard That using the text boxes and diagrams, or you could print them off and have students complete them by hand. Another great way to use them is to project the planning sheet onto an interactive whiteboard and work through how to complete the planning materials as a group. Project it onto a screen and have students write their answers on sticky notes and put their ideas in the correct section of the planning document.

Very young learners can still start to think as scientists! They have loads of questions about the world around them and you can start to make a note of these in a mind map. Sometimes you can even start to ‘investigate’ these questions through play.

The foundation resource is intended for elementary students or students who need more support. It is designed to follow exactly the same process as the higher resources, but made slightly easier. The key difference between the two resources are the details that students are required to think about and the technical vocabulary used. For example, it is important that students identify variables when they are designing their investigations. In the higher version, students not only have to identify the variables, but make other comments, such as how they are going to measure the dependent variable or utilizing completely randomized design. As well as the difference in scaffolding between the two levels of resources, you may want to further differentiate by how the learners are supported by teachers and assistants in the room.

Students could also be encouraged to make their experimental plan easier to understand by using graphics, and this could also be used to support ELLs.

Students need to be assessed on their science inquiry skills alongside the assessment of their knowledge. Not only will that let students focus on developing their skills, but will also allow them to use their assessment information in a way that will help them improve their science skills. Using Quick Rubric , you can create a quick and easy assessment framework and share it with students so they know how to succeed at every stage. As well as providing formative assessment which will drive learning, this can also be used to assess student work at the end of an investigation and set targets for when they next attempt to plan their own investigation. The rubrics have been written in a way to allow students to access them easily. This way they can be shared with students as they are working through the planning process so students know what a good experimental design looks like.

Printable Resources

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Related Activities

Additional Worksheets

If you're looking to add additional projects or continue to customize worksheets, take a look at several template pages we've compiled for you below. Each worksheet can be copied and tailored to your projects or students! Students can also be encouraged to create their own if they want to try organizing information in an easy to understand way.

• Lab Worksheets
• Discussion Worksheets
• Checklist Worksheets

Related Resources

• Scientific Method Steps
• Science Discussion Storyboards
• Developing Critical Thinking Skills

How to Teach Students the Design of Experiments

Encourage questioning and curiosity.

Foster a culture of inquiry by encouraging students to ask questions about the world around them.

Formulate testable hypotheses

Teach students how to develop hypotheses that can be scientifically tested. Help them understand the difference between a hypothesis and a question.

Provide scientific background

Help students understand the scientific principles and concepts relevant to their hypotheses. Encourage them to draw on prior knowledge or conduct research to support their hypotheses.

Identify variables

Teach students about the three types of variables (dependent, independent, and controlled) and how they relate to experimental design. Emphasize the importance of controlling variables and measuring the dependent variable accurately.

Plan and diagram the experiment

Guide students in developing a clear and reproducible experimental procedure. Encourage them to create a step-by-step plan or use visual diagrams to illustrate the process.

Carry out the experiment and analyze data

Support students as they conduct the experiment according to their plan. Guide them in collecting data in a meaningful and organized manner. Assist them in analyzing the data and drawing conclusions based on their findings.

Frequently Asked Questions about Experimental Design for Students

What are some common experimental design tools and techniques that students can use.

Common experimental design tools and techniques that students can use include random assignment, control groups, blinding, replication, and statistical analysis. Students can also use observational studies, surveys, and experiments with natural or quasi-experimental designs. They can also use data visualization tools to analyze and present their results.

How can experimental design help students develop critical thinking skills?

Experimental design helps students develop critical thinking skills by encouraging them to think systematically and logically about scientific problems. It requires students to analyze data, identify patterns, and draw conclusions based on evidence. It also helps students to develop problem-solving skills by providing opportunities to design and conduct experiments to test hypotheses.

How can experimental design be used to address real-world problems?

Experimental design can be used to address real-world problems by identifying variables that contribute to a particular problem and testing interventions to see if they are effective in addressing the problem. For example, experimental design can be used to test the effectiveness of new medical treatments or to evaluate the impact of social interventions on reducing poverty or improving educational outcomes.

What are some common experimental design pitfalls that students should avoid?

Common experimental design pitfalls that students should avoid include failing to control variables, using biased samples, relying on anecdotal evidence, and failing to measure dependent variables accurately. Students should also be aware of ethical considerations when conducting experiments, such as obtaining informed consent and protecting the privacy of research subjects.

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• A Quick Guide to Experimental Design | 5 Steps & Examples

A Quick Guide to Experimental Design | 5 Steps & Examples

Published on 11 April 2022 by Rebecca Bevans . Revised on 5 December 2022.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design means creating a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying. 

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If if random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, frequently asked questions about experimental design.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

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Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalised and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomised design vs a randomised block design .
• A between-subjects design vs a within-subjects design .

Randomisation

An experiment can be completely randomised or randomised within blocks (aka strata):

• In a completely randomised design , every subject is assigned to a treatment group at random.
• In a randomised block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomisation isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomising or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimise bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalised to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

Experimental designs are a set of procedures that you plan in order to examine the relationship between variables that interest you.

To design a successful experiment, first identify:

• A testable hypothesis
• One or more independent variables that you will manipulate
• One or more dependent variables that you will measure

When designing the experiment, first decide:

• How your variable(s) will be manipulated
• How you will control for any potential confounding or lurking variables
• How many subjects you will include
• How you will assign treatments to your subjects

The key difference between observational studies and experiments is that, done correctly, an observational study will never influence the responses or behaviours of participants. Experimental designs will have a treatment condition applied to at least a portion of participants.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word ‘between’ means that you’re comparing different conditions between groups, while the word ‘within’ means you’re comparing different conditions within the same group.

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Experimental Design: The Complete Pocket Guide

Bryn Farnsworth

Our comprehensive manual on experimental design provides guidance on avoiding common mistakes and pitfalls when establishing the optimal experiment for your research.

Table of Contents

• Introduction to experimental methods

Humans are a quite curious species. We explore new grounds, improve products and services, find faster and safer ways to produce or transport goods, and we solve the mysteries of global diseases. All of these activities are guided by asking the right questions, by searching for answers in the right spots and taking appropriate decisions. Academic and commercial research have professionalized this quest for knowledge and insights into ourselves and the world surrounding us.

Every day, research institutions across the globe investigate the inner workings of our universe – from cellular levels of our synapses and neurons to macroscopic levels of planets and solar systems – by means of experimentation. Simply put: Experiments are the professional way to answer questions, identify cause and effect or determine predictors and outcomes. These insights help us understand how and why things are what they are and can ultimately be used to change the world by improving the good and overcoming the bad.

N.B. this post is an excerpt from our Experimental Design Guide. You can download your free copy below and get even more insights into the world of Experimental Design.

Free 44-page Experimental Design Guide

For Beginners and Intermediates

• Respondent management with groups and populations
• How to set up stimulus selection and arrangement

In contrast to the early years of scientific research, modern-age experiments are not merely results of scientists randomly probing assumptions combined with the pure luck to be at the right place at the right time and observe outcomes.

Today’s scientific insights are the result of careful thinking and experimental planning, proper collecting of data, and drawing of appropriate conclusions.

Experimental Design Example

Researchers use experiments to learn something new about the world, to answer questions or to probe theoretical assumptions.

Typical examples for research questions in human cognitive-behavioral research are:

• How does sensory stimulation affect human attention? How do, for example, moving dot patterns, sounds or electrical stimulation alter our perception of the world?

• What are the changes in human physiology during information uptake? How do heart rate and galvanic skin response, for example, change as we recall correct or incorrect information?

• How does virtual reality compared to real physical environments affect human behavior? Do humans learn faster in the real world compared to VR?

• How does stress affect the interaction with other colleagues or machines in the workplace?

• How does packaging of a product affect shoppers’ frustration levels? Is the new package intuitive to open, and if not, how does it affect the behavior of the person?

• How does the new TV commercial impact on emotional expressions and brand memory? Does gender have an influence on purchase decisions after watching the ad?

• How does a website affect users’ stress levels in terms of galvanic skin response, ECG and facial expressions?

• Which intersections in town cause most frustration in bicyclists?

• What are the aspects in a presidential campaign speech that drive voters’ decisions?

As you can see, research questions can be somewhat generic. Experiments are supposed to clarify these questions in a more standardized framework. In order to do so, several steps are necessary to fine-tune the research question into a more testable form:

Step 1: Phrase a hypothesis

First, the general research question is broken down into a testable hypothesis or several hypotheses. Hypotheses are explicit statements about cause and effect and address what outcomes occur when specific factors are manipulated:

Hypotheses phrase a relationship between one or more independent variables and one or more dependent variables:

•Independent variable

The independent variable (IV) is strategically changed, or manipulated, by the experimenter. IVs are also referred to as factors.

• Dependent variable (DV)

The dependent variable (DV) is measured by the experimenter. Experiments with one DV are called univariate, experiments with two or more DV are called multivariate.

The general research question “How does stress affect the interaction with others? ” might lead to the following hypotheses about how stress (independent variable) affects interaction with others (dependent variable):

1) “Having to reply to 100 or more incoming emails per hour results in reduced verbal interaction with colleagues.”

Independent variable: Number of emails per hour Dependent variable: Number of verbal interactions with colleagues per hour

2) “Sleeping 8 hours or more per night results in increased informal sport activities with colleagues.”

Independent variable : Duration of sleep per night Dependent variable : Number of sport meetups with colleagues per week

3) “Regular physical exercise in the evening results in increased occurrences of smiles when talking to others in business meetings.”

Independent variable : Number of evening sport activities per week Dependent variable : Smile occurrences when talking with others

Hypotheses make the research question more explicit by stating an observable relationship between cause and effect. Hypotheses also determine which stimuli are used and what respondents are exposed to.

A stimulus doesn’t have to be just pictures or tones, much more constitutes a stimulus, for example, questionnaires, websites, videos, speech and conversations with others, visual and proprioceptive input while driving and much more. We will address stimuli in more detail below.

Step 2: Sample Groups

Define sample groups.

After specifying the hypothesis, you need to clarify the respondent group characteristics for your experiment. This step is necessary to exclude side effects that could alter the outcomes of your experimental data collection. Make sure that demographic characteristics such as age, gender, education level, income, marital status, occupation etc. are consistent across the respondent pool. Individual characteristics such as state of health or exposure to certain life events should be considered as they might affect experimental outcomes. For example, mothers might respond differently to a TV ad for baby toys than women without kids. Soldiers suffering from PTSD might respond differently to stress-provoking stimuli than software developers.

Step 3: Assign subjects to groups

In this step, you randomly distribute subjects to the different experimental conditions. For example, for your stress in the workplace study you could create two experimental groups, where group one receives 10 emails per hour, and group two receives 100 emails per hour. You could now analyze how the two groups differ in their social interaction with others within the next 6 hours. Ideally, the assignment to experimental groups is done in a randomized fashion, such that all respondents have the same probability for ending up in the available experimental groups. There should not be any bias to assign specific respondents to one group or the other.

Step 4: Determine sampling frequency.

How often do you want to measure from respondents? Clinical trials typically measure patients’ state of health once per month over the course of several months or years. In usability studies you might ask respondents once at the end of the session several questions, either verbally or via surveys and questionnaires.

However, when you collect cognitive-behavioral data from EEG, EMG, ECG, GSR or other biosensors while respondents are doing a specific task, you are collecting tens to hundreds of data points per second – even though all of these sub-second samples might be used to compute an overall score reflecting a certain cognitive or affective state. We will address later in this guide which sensors are ideal to collect specific cognitive-behavioral metrics.

Step 5: Conduct the experiment and collect data.

In this step, you execute the experimental paradigm according to the selected methods. Make sure to observe, monitor, and report any important moments during data collection. Prior to conducting the experiment, run a pilot test to rule out any issues that might arise during data collection (stimulus was wrong length/non-randomized/not optimal, etc.)

Check out : 7 Tips & Tricks For a Smooth Lab Experience

Step 6: Pre-process data and analyze metrics.

In human cognitive-behavioral research, raw data can consist of self-reports or data from biosensors. Of course, video footage of experimental sessions such as focus groups and interviews also constitute raw data and have to be analyzed using coding schemes. Due to the wide range of statistical methods to analyze raw data and metrics, we will not address this step in the current guide. However, one crucial aspect should be mentioned here: The selection of a specific statistical method for data analysis should always be driven by the original hypothesis and the collected data.

Of course, not all experiments require the precise specification of all of these steps. Sometimes you as a researcher don’t have control of certain factors, or you are lacking access to specific respondent populations. Dependent on the amount of control that you have over the relationship between cause and effect, the following types of experiments can be distinguished:

Types of Experimental design

1. laboratory experiments.

Whenever we speak informally of experiments, lab experiments might come to mind where researchers in white lab coats observe others from behind one-side mirrors, taking minute notes on the performance and behavior of human participants executing key-press tasks in front of somewhat unpredictable machines. In fact, this is how human cognitive-behavioral research started (see the Milgram experiment ).

Gladfully, the days of sterile lab environments are long gone, and you can run your study wearing your favorite sweater. However, a core aspect still holds: Being able to control all factors and conditions that could have an effect. For example, in lab experiments you can select specific respondent groups and assign them to different experimental conditions, determine the precise timing and configuration of all stimuli, and exclude any problematic side-effects.

What you should know about laboratory experiments…

• Precise control of all external and internal factors that could affect experimental outcomes.
• Random assignment of respondents to experimental groups, ideally by means of randomization.
• Allows identification of cause-effect relationships with highest accuracy.
• Since everything is standardized, others can replicate your study, which makes your study more “credible” compared to non-standardized scenarios.

Limitations.

• Controlled experiments do not reflect the real world. Respondents might not respond naturally because the lab doesn’t reflect the natural environment. In technical terms, lab experiments are lacking ecological validity.
• Observer effects might change respondents’ behavior. An experimenter sitting right next to a respondent or observing them via webcam might bias experimental outcomes (read up on the Hawthorne Effect ).

2. Field experiments

In contrast to lab experiments, field experiments are done in the natural surroundings of respondents. While the experimenter manipulates the “cause”-aspect, there’s no control of what else could potentially affect the effects and outcomes (such as the Hofling’s Hospital Experiment based on Milgram‘s work).

Quite often, engineers also conduct field tests of prototypes of soft- and hardware to validate earlier lab tests and to obtain broader feedback from respondents in real life.

What you should know about field experiments…

>>  strengths..

• Field experiments reflect real-life scenarios more than lab experiments. They have higher ecological validity
• When experiments are covert and respondents don’t feel observed, the observed behavior is much closer to real life compared to lab settings.

>> Limitations.

• No control over external factors that could potentially affect outcomes. The outcomes are therefore much more varied. More respondents are therefore needed to compensate the variation.
• Difficult to replicate by others.
• Limited ability to obtain informed consent from respondents.

3. Natural experiments.

Natural experiments are pure observation studies in the sense that the experimenter doesn’t have any control. Respondent groups are observed as-is and not strategically assigned to different experimental conditions.

You might want to compare existing iPhone and Android users, people living close to Chernobyl and people living somewhere else, or patients suffering from cancer and healthy populations. In this case, the groups that you’d like to compare already exist by nature – you don’t have to create them.

What you should know about natural experiments…

• Behavior in natural experiments more likely reflects real life.
• Ideal in situations where it would be ethically unacceptable to manipulate the group assignment (e.g., expose respondents to radiation).
• More expensive and time-consuming than lab experiments.
• No control over any factors implies that replication by others is almost impossible.

How can I measure human behavior?

Laboratory, field and natural experiments all have one aspect in common: Insights are accomplished empirically. “Empirical” means that research questions and hypotheses are not answered by mere reflection or thought experiments.

Instead of leaning back in a chair and pondering over the potential outcomes of a thought experiment, researchers in human cognitive-behavioral science accomplish their work by means of active observation and probing of the environment in order to identify the underlying processes as well as the ultimate “driving forces” of human behavior.

Within the last decades, researchers have developed intricate experimental techniques and procedures that have found their way also into commercial testing of emotional, cognitive and attentional effects of new products and services, or how personality traits and problem-solving strategies have an impact on brand likeability and consumer preferences.

Two ways to study Human Behavior

Qualitative studies on human behavior.

Qualitative studies gather observational insights. Examples include the investigation of diary entries, open questionnaires, unstructured interviews or observations. Because nothing is counted or quantified and every observation is described as-is, qualitative data is also referred to as descriptive.

In qualitative field studies or usability studies, for example, researchers directly observe how respondents are using the technology, allowing them to directly ask questions, probe on behavior or potentially even adjust the experimental protocol to incorporate the individual’s behavior. The focus of qualitative studies is primarily on understanding how respondents see the world and why they react in a specific way.

What you should know about qualitative studies…

•  Ideal to answer “why” and “how to fix a problem?” questions.
• Focus on individual experience of the respondent.
• Small respondent samples required.
• Knowledge gained in the specific study might not be transferrable to other groups.
• Data collection might take longer per respondent.
• Risk that results are affected by researcher’s biases and preferences.

Typical use cases.

•  UX, web and software usability tests (description of user journeys).
• Open-ended interviews and surveys on biographical events.
• Focus groups with / without experimenter present.

Check out: How to Deliver better UX with Emotion Detection 

Quantitative studies

Quantitative studies by contrast, quantitative studies characterize the systematic empirical investigation of observable phenomena via statistical, mathematical or computational techniques. In other words, quantitative studies use numbers to describe and characterize human behavior.

Examples for quantitative techniques include structured surveys and interviews, observations with dedicated coding schemes (e.g., counting the number of cigarettes smoked within a day), or physiological measurements from EEG, EMG, ECG, GSR and other sensors producing numerical output. Whenever researchers are using quantitative methods, they translate behavioral observations into countable numbers and statistical outputs. All of this is done to guarantee maximum experimental control.

What you should know about quantitative studies…

• Ideal for answering “how many” and “how much” questions.
• Useful to analyze large respondent groups, focus on entire populations.
• High amount of standardization requires less time than qualitative studies.
• Provides numerical values that can be analyzed statistically.
• Experimenter might miss out phenomena because the measurement tool is too narrow.
• Contextual factors are often ignored or missing.
• Studies are expensive and time-consuming.
• Behavioral observation using coding schemes (e.g., on facial expressions or action occurrences within a certain time frame)
• Structured interviews and surveys containing single- or multiple-choice questions as well as scales.
• Physiological measurements of bodily processes (EEG, EMG, GSR etc.)

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Check out: Qualitative vs Quantitative Research 

Which numbers could human cognitive-behavioral research potentially use to describe our complex inner workings, our intelligence, personality traits or skill levels? What are measurable indicators of a person being a shopaholic, for example?

Indicators that can be counted might be the average time spent in department stores during a week, the cumulative amount of money laid out for certain lifestyle products, or the number of shoe boxes filling up the closet under the stairs (have a look at our reading tip on measurement and the assignment of numbers or events).

The basic principle is that hidden factors of our personality can be made visible (and therefore measurable) by breaking them into feasible and tangible, graspable and observable units which can be counted numerically. This “making visible” of latent constructs of our personality and identity is referred to as operationalization.

While some measures are more suitable to capture an underlying latent characteristic, others might fail. So the question is, what actually constitutes an appropriate measure?

Measurements to avoid bias

This is generally described with respect to the following criteria:

Objectivity

Objectivity is the most general requirement and reflects the fact that measures should come to the same result no matter who is using them. Also, they should generate the same outcomes independent of the outside influences. For example, a multiple-choice personality questionnaire or survey is objective if it returns the same score irrelevant of whether the participant responds verbally or in written form. Further, the result should be independent of the knowledge or attitude of the experimenter, so that the results are purely driven by the performance of the respondent.

Reliability

A measure is said to have high reliability if it returns the same value under consistent conditions. There are several sub-categories of reliability. For example, “retest reliability” describes the stability of a measure over time, “inter-rater reliability” reflects the amount to which different experimenters give consistent estimates of the same behavior, while “split-half reliability” breaks a test into two and examines to what extent the two halves generate identical results.

This is the final and most crucial criterion. It reflects the extent to which a measure collects what it is supposed to collect. Imagine an experiment where body size is collected to measure its relationship with happiness. Obviously, the measure is both objective and reliable (body size measures are quite consistent irrespective of the person taking the measurement) but it is truly a poor measure with respect to its construct validity (i.e., its capability to truly capture the underlying variable) for happiness.

Once you have identified measures that fulfill objectivity, reliability and validity criteria at the same time, you are on the right track to generate experimental outcomes that will push beyond the frontiers of our existing knowledge.

Respondent Management

While Iceland has research programs where experiments are applied to the entire nation, other countries and situations do not allow testing everybody. Of course, it would grant maximum insights into your research question, but due to time and resource constraints studies and experiments are generally carried out on respondent groups rather than entire populations.

The most challenging part is to find respondents that truly represent the larger target population allowing you to generalize, or infer, from your study group findings to the population. You might have heard the phrase “representative sample” before. This describes respondent groups where each and every member of the population has an equal chance of being selected for your experiment. Populations don’t necessarily have to be entire countries – the term simply reflects “all people that share certain characteristics” (height, weight, BMI, hemoglobin levels, experience, income, nationality etc.) which are considered relevant for your experiment.

Exemplary populations are:

• Female academics between 30 and 40 years in the US with an average annual income of $50k
• Software developers with more than 5 years of experience in C#
• Patients suffering from secondary progressive Multiple Sclerosis
• After-work shoppers of any age and gender
• Danish mothers up to 50 years
• People wearing glasses

A sample now can be a group of 100 Multiple Sclerosis patients, or 20 dog owners. Finding “representative samples” is not that easy as there is some bias in almost all studies. Samples can be found as following:

Non-random respondent sampling

Non-random sampling can be done during initial pre-screening phases, where generalization is not important. In that case, the experimental outcomes only apply to the tested respondent group. Sampling is done as following:

• Volunteers . You ask people on the street, and whoever agrees to participate is tested.
• Snowball sample . One case identifies others of his kind (e.g., HSE shoppers).
• Convenience sample . You test your co-workers and colleagues or other readily available groups.
• Quota sample . At-will selection of a fixed number from several groups (e.g., 30 male and 30 female respondents).

Random respondent sampling .

Random sampling is actually giving everyone in the population the same chance of being included in your experiment. The benefit of being able to conclude from your research findings obtained from several respondents to the general public comes, however, with high demands on time and resources. The following random sampling strategies exist:

Simple random sampling

In random samples chances for everyone are identical to being included in your test. This means that you had to identify, for example, every female academic between 30 and 40 years in the US with an average annual income of $50k, or every dog owner. Subsequently, you draw random samples and only contact those. Random sampling disallows any selection bias based on volunteering or cooperation.

Systematic sampling

Instead of a completely random selection, you systematically select every nth person from an existing list, for example ordered by respondent age, disease duration, membership, distance etc.

Multistage sampling

Sampling can be done in multiple steps. For example, to find representative students for testing, you can first draw a random selection of counties, then proceed with random drawing of cities, schools, and classes. Finally, you randomly draw students for observation and recording.

Cluster sampling

Particularly for self-reports, studies are carried out on large and geographically dispersed populations. In order to obtain the required number of respondents for testing, clusters may be identified and randomly drawn. Subsuequently, all members of the drawn samples are tested. For example, clustering might be done using households – in this case, all household members are tested, reducing the time and resources for testing massively.

Which sampling method you use is generally determined by feasibility in terms of time and resources. It might often be difficult to obtain truly random samples, particularly in field research. You can find more details on suggested procedures for representative sampling in Banerjee and colleagues (2007; 2010).

How many respondents do I need?

Sampling strategies are closely linked to the sample size of your experiment. If you would like to do a single case study, of course only one respondent is needed. In this case, however, you cannot generalize any findings to the larger population. On the other hand, sampling from the entire population is not possible. The question is, how many respondents are suitable for your experiment? What is the ideal sample size?

Martinez and colleagues (2014) as well as Niles (2011) provide recommendations. Without delving too deep into statistics, the main message is about this: Always collect as many respondents as necessary. For quantitative usability testing 20 respondents might be sufficient, but more respondents should be tested whenever the expected effects are smaller, for example, if there’s only subtle differences between the different stimulus conditions.

This is why academic researchers run studies with dozens to hundreds or thousands of respondents. With more respondents, you reduce the ambiguity of individual variation that could have affected experimental outcomes.Top of Page

The amount of security about your findings is typically expressed with respect to confidence, which is roughly expressed with the following formula:

N is the sample size. As you can see, higher respondent samples cause confidence to become smaller (which is the desired outcome). In other words, testing more people gives you more accurate results.

For example, if you tested the preference for a new product with 10 out of 10,000 respondents, then the confidence is at 32%. If 7 out of 10 respondents (70%) liked the new product, the actual proportion in the population could be as low as 48% (70-32) and as high as 100% (70+32, you can’t be above 100). With a variation from 48% to 100%, your test might not be that helpful.

If you increase the sample size to 100 respondents out of 10,000, the confidence is at 10%. With 70 out of 100 respondents liking the product, the actual value in the population is somewhere between 60% and 80%. You’re getting much closer!

If you would like to further reduce the confidence to 5%, you have to test at least 500 randomly-selected respondents. The bottom line is, you have to test lots of respondents before being able to get to conclusions. For more information visit the Creative Research Systems website , where you can find a more exact formula as well as a sample size calculator tool.

Cross-sectional vs. longitudinal designs

Experimental design and the way your study is carried out depends on the nature of your research question. If you’re interested in how a new TV advertisement is perceived by the general public in terms of attention, cognition and affect, there’s several ways to design your study. Do you want to compare cognitive-behavioral outcomes of the ad among different populations of low and high-income households at the same point in time? Or, do you want to measure the TV ad effects in a single population (say, male high-income shoppers with specific demographic characteristics) over an extended period of time? The former approach is generally referred to as cross-sectional design. The latter is called longitudinal design. The two can further be combined (mixed design)

Cross-sectional design

In cross-sectional studies two or more groups are compared at a single point in time. Similar to taking a snapshot, every respondent is invited and tested just once. In our example, you would show the new TV ad to respondents from low- and high-income households. You would not, however, invite them and show them the TV ad again a week later.

Other examples of cross-sectional studies are:

• Gaming. Compare effects of video games on emotional responsiveness of healthy children and children suffering from ADHS.
• Web testing. Compare website usability evaluation of young, middle-aged and senior shoppers.
• Psychology. Compare evaluation of parenting style of mothers and fathers.

The primary benefit of a cross-sectional experimental design is that it allows you to compare many different variables at the same time. You could, for example, investigate the impact of age, gender, experience or educational levels on respondents’ cognitive-emotional evaluation of the TV ad with little or no additional cost. The only thing you have to do is collect the data (for example, by means of interviews or surveys).

Longitudinal design

In a longitudinal study you conduct several observations of the same respondent group over time, lasting from hours to days, months and many years. By doing this, you establish a sequence of events and minimize the noise that could potentially affect each of the single measurements. In other words, you simply make the outcomes more robust against potential side effects.

For example, you could show a TV ad several times to your group of interest (male high-income shoppers) and see how their preference for the ad changes over time.

Other examples for longitudinal designs are:

• Media / package testing. Two or more media trailers or packages are shown in sequence to a group of respondents who evaluate how much they like each of the presented items.
• Food and flavor testing. Respondents are exposed to two or more flavors presented in sequence and asked for their feedback.
• UI and UX testing. Respondents navigate two or more websites and are interviewed with respect to usability questions.
• Psychology and Training. A group of respondents attending a professional training session answers a questionnaire on emotional well-being before, during and after training.
• Physiology. You monitor EEG, GSR, EMG, facial expressions, etc. while respondents are exposed to pictures, sounds or video stimuli.

The primary benefit of longitudinal designs is that you obtain a time-course of values within one group of respondents. Even if you only obtain cognitive-affective test scores before and after the experimental intervention, you are more likely to understand the impact of the intervention on already existing levels of attention, cognition or affect. Therefore, longitudinal studies are more likely to suggest cause-and-effect relationships than cross-sectional studies.

Mixed design

Mixed designs combine the best of two worlds as they allow you to collect longitudinal data across several groups. Strictly spoken, whenever you collect physiological data (like EEG, GSR, EMG, ECG, facial expressions, etc.) from several respondent groups in order to compare different populations, you have a mixed study design. The data itself is longitudinal (several samples over time), while the group comparison has cross-sectional aspects.

Typical examples for mixed designs are:

• Product / media testing. Two or more versions of a product or service are compared with respect to cognitive-behavioral outcomes of two or more groups (e.g., novices and experts, male and female, young and old).
• A-B testing. Two versions of a website or app are compared with respect to cognitive-behavioral outcomes of two or more groups.

Mixed design experiments are ideal for collecting time-courses across several groups of interest, allowing you to investigate the driving forces of human behavior in more detail than cross-sectional or longitudinal designs alone.

Ultimately, which design you choose is driven primarily by your research question. Of course, you can run a cross-sectional study first to get an idea of the potential factors affecting outcomes, and then do a more fine-grained longitudinal study to investigate cause and effect in more detail.

In the next section we will explain in more detail how stimuli should be arranged and which sensors are relevant.

Selecting and arranging stimuli

Experiments in human cognitive-behavior research typically involve some kind of stimulation used to evoke a reaction from respondents. The two most crucial stimulus-related questions are: Which stimuli do I need? In which sequence shall I present the stimuli?

Types of stimuli

Stimuli come in a range of modalities including audio, visual, haptic, olfactory etc. Multimodal stimuli combine several modalities. The following stimuli are used in academic and commercial research studies on human behavior:

• Images / pictures
• Software interfaces
• Devices (car interieur, aircraft cockpit, milkshake machine etc.)
• Communication with others via phone, web or face-to-face
• Complex scenes (VR, real environments)
• Sound (sine waves, complex sound, spoken language, music)
• Olfaction (flavors, smells)
• Haptic stimuli (object exploration by touch, pressure plates, vibrating sensors, haptic robots)
• Questionnaires and surveys (web- or software-based, paper and pencil)

Stimulus sequence

Stimuli are generally presented to respondents in a specific sequence. What are typical sequences used in human cognitive-behavioral research?

Fixed stimulus sequence

Fixed sequences are necessary whenever randomized sequences do not make sense or cannot be employed. For example, when combining a website test with a website-related interview it doesn’t make sense to ask website-related questions first and then tell the respondent to actually use the website.

Here, the only meaningful sequence is to do the website exploration first and the questionnaire second. When it comes to comparing different versions of a stimulus, for example, websites A and B, fixed sequences can also be used.

Random stimulus sequence

As you have learned before, presenting stimuli in the same sequence to all respondents bears the risk of sequential effects. Respondents might rate the first stimulus always higher because they are still motivated, engaged and curious.

After two long hours at the lab, exhaustion might take over, so ratings might be low even if the tested product or service exceeds all previous expectations. This can be avoided by presenting stimuli in random order.

Counterbalanced sequence

To avoid the issues of complete randomization, counterbalanced designs try to achieve an even distribution of conditions across the stimulus slots of the experiment. In the example below, two stimulus conditions A and B are counterbalanced across six respondents, so that three respondents are exposed to stimulus A first, and the other three respondents are exposed to stimulus B first.

Block design

Sometimes it doesn’t make sense to randomize the entire stimulus list as there might be some internal logic and sequence. Let’s assume you would like to evaluate respondents’ behavior when unpacking several food packages.

For each package, there’s a fixed evaluation protocol where (a) the package is unveiled and (b) respondents are asked to describe their associations verbally. Then, (c) they should pick up the package and open it and (d) describe their experience. This sequence from step (a) to (d) can also be characterized as an experimental “block”, which is supposed to be repeated for all tested packages.

While the package presentation sequence is randomized, the content of each of the blocks stays the same.

Repeated design

EEG and other physiological recordings sometimes require repeated presentations of the same stimulus. This is necessary because the stimulus-driven changes in brain activity are much smaller compared to the ongoing activity. Presenting the same stimulus several times makes sure that enough data is present to get to valid conclusions.

However, stimulus repetition can also be done for eye tracking studies. In this case, the randomization procedures listed above apply as well.

You might be interested in the number of repetitions necessary to get to results. Unfortunately, this cannot be answered globally, as it depends on several factors such as magnitude of the expected effect/difference between two conditions, stimulus modality, physiological effect of interest, and other factors that take impact on experimental outcomes.

Also, there are strong statistical considerations which are beyond the scope of this general introduction.

Modalities and sensors

Whenever you design experiments for human cognitive-behavior research, you certainly want to consider which biosensors you collect data from. Human behavior is a complex interplay of a variety of different processes, ranging from completely unconscious modulations of emotional reactions to decision-making based on conscious thoughts and cognition. In fact, each of our emotional and cognitive responses is driven by factors such as arousal, workload, and environmental conditions that impact our well-being in that very moment.

All of these aspects of human behavior can be captured by self-reports (via interviews or surveys), specific devices (such as eye trackers, EEG systems, GSR and ECG sensors ) or camera-based facial expression analysis.

TV ads, video games, movies, websites, devices as well as social interaction partners in private life and in the workplace – we could process none of these without our vision. The human brain is fine-tuned for visual input and controlling eye movements. Therefore, it makes immediate sense to collect information on gaze position and pupil dilation from eye tracking. If you present visual stimuli on screen, you should always collect eye tracking data to be absolutely sure where respondents are directing their gaze to and how this is affecting cognitive processing. Second, monitoring pupil dilation can give valuable insights into arousal and stress levels of a respondent. As pupil dilation is an autonomic process, it cannot be controlled consciously. Eye tracking recordings allow you to monitor both respondents’ engagement and motivation as well as arousal levels during the encounter with emotional or cognitively challenging stimuli.

Galvanic skin response (GSR) or electrodermal activity (EDA) reflects the amount of sweat secretion from sweat glands in our skin. Increased sweating results in higher skin conductivity. When exposed to emotional content, we sweat emotionally. GSR recordings in conjunction with EEG are extremely powerful as skin conductance is controlled subconsciously, that is, by deeper and older brain structures than the cognitive processes that are monitored by EEG. Therefore, adding GSR offers tremendous insights into the unfiltered, unbiased emotional arousal of a respondent.

Facial Expression Analysis

With facial expression analysis you can assess if respondents are truly expressing their positive attitude in observable behavior. Facial expression analysis is a non-intrusive method to assess head position and orientation (so you always know where your respondents are positioned relative to the stimulus), expressions (such as lifting of the eyebrows or opening of the mouth) and global facial expressions of basic emotions (joy, anger, surprise etc.) using a webcam placed in front of the respondent. Facial data is extremely helpful to monitor engagement, frustration or drowsiness.

(facial) EMG

Electromyographic sensors monitor the electric energy generated by body movements. EMG sensors can be used to monitor muscular responses of the face, hands or fingers in response to any type of stimulus material. Even subtle activation patterns associated with consciously controlled hand/finger movements (startle reflex) can be assessed with EMG. Collecting synchronized EMG data is relevant for anyone interested in how movements of the eyes and limbs are prepared and executed, but also how movements are prevented and actions are inhibited.

Monitoring heart activity with ECG electrodes attached to the chest or optical heart rate sensors attached to finger tips allows you to track respondents’ physical state, their anxiety and stress levels (arousal), and how changes in physiological state relate to their actions and decisions. Tracking respondents’ physical exhaustion with ECG sensors can provide helpful insights into cognitive-affective processes under bodily straining activity.

Electroencephalography (EEG) is a neuroimaging technique measuring electrical activity generated by the brain from the scalp surface using portable sensors and amplifier systems. It undoubtedly is your means of choice when it comes to assess brain activity associated with perception, cognitive behavior, and emotional processes. EEG reveals substantial insights into sub-second brain dynamics of engagement, motivation, frustration, cognitive workload, and further metrics associated with stimulus processing, action preparation, and execution. Simply put: EEG impressively tells which parts of the brain are active while we perform a task or are exposed to certain stimulus material.

Self-reports

Any experiment should contain self-reported data collection stages, for example at the beginning of the session, during data collection , and at the very end. Gathering demographic data (gender, age, socio-economical status, etc.) helps describing the respondent group in more detail. Also, self-reported data from interviews and surveys helps tremendously to gain insights into the subjective world of the respondents – their self-perceived levels of attention, motivation and engagement – beyond quantitative values reported by biosensors. Of course, survey results can be utilized to segment your respondents into specific groups for analysis (e.g., young vs. old; male vs. female; novice vs. experienced users).

Experimental design done right with iMotions

Properly designed experiments allow you deep insights into attention, cognition and emotional processing of your desired target population when confronted with physical objects or stimuli. Experimental research has come up with dedicated recommendations on how to prevent experimenter or segmentation bias – randomization strategies for respondent and stimulus selection are an excellent starting point.

Before you get started designing your next human cognitive-behavioral experiment, you certainly want to think about how to arrange stimuli, how to select respondents and which biosensors to use in order to gain maximum insights.

What if there was a multimodal software solution that allows for loading and arranging any type of stimuli, for example, in fixed or randomized sequences, while recording data from EEG, eye tracking, facial expression analysis and other biosensors (such as GSR, ECG, EMG) without having to manually piece everything together?

The iMotions Platform

The iMotions Platform is one easy-to-use software solution for study design, multi-sensor calibration, data collection, and analysis.

Out of the box, iMotions supports over 50 leading biosensors including facial expression analysis, GSR, eye tracking, EEG, ECG, and EMG, as well as surveys for multimodal human behavior research.

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Experimental Design: Types, Examples & Methods

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Experimental design refers to how participants are allocated to different groups in an experiment. Types of design include repeated measures, independent groups, and matched pairs designs.

Probably the most common way to design an experiment in psychology is to divide the participants into two groups, the experimental group and the control group, and then introduce a change to the experimental group, not the control group.

The researcher must decide how he/she will allocate their sample to the different experimental groups.  For example, if there are 10 participants, will all 10 participants participate in both groups (e.g., repeated measures), or will the participants be split in half and take part in only one group each?

Three types of experimental designs are commonly used:

1. Independent Measures

Independent measures design, also known as between-groups , is an experimental design where different participants are used in each condition of the independent variable.  This means that each condition of the experiment includes a different group of participants.

This should be done by random allocation, ensuring that each participant has an equal chance of being assigned to one group.

Independent measures involve using two separate groups of participants, one in each condition. For example:

• Con : More people are needed than with the repeated measures design (i.e., more time-consuming).
• Pro : Avoids order effects (such as practice or fatigue) as people participate in one condition only.  If a person is involved in several conditions, they may become bored, tired, and fed up by the time they come to the second condition or become wise to the requirements of the experiment!
• Con : Differences between participants in the groups may affect results, for example, variations in age, gender, or social background.  These differences are known as participant variables (i.e., a type of extraneous variable ).
• Control : After the participants have been recruited, they should be randomly assigned to their groups. This should ensure the groups are similar, on average (reducing participant variables).

2. Repeated Measures Design

Repeated Measures design is an experimental design where the same participants participate in each independent variable condition.  This means that each experiment condition includes the same group of participants.

Repeated Measures design is also known as within-groups or within-subjects design .

• Pro : As the same participants are used in each condition, participant variables (i.e., individual differences) are reduced.
• Con : There may be order effects. Order effects refer to the order of the conditions affecting the participants’ behavior.  Performance in the second condition may be better because the participants know what to do (i.e., practice effect).  Or their performance might be worse in the second condition because they are tired (i.e., fatigue effect). This limitation can be controlled using counterbalancing.
• Pro : Fewer people are needed as they participate in all conditions (i.e., saves time).
• Control : To combat order effects, the researcher counter-balances the order of the conditions for the participants.  Alternating the order in which participants perform in different conditions of an experiment.

Counterbalancing

Suppose we used a repeated measures design in which all of the participants first learned words in “loud noise” and then learned them in “no noise.”

We expect the participants to learn better in “no noise” because of order effects, such as practice. However, a researcher can control for order effects using counterbalancing.

The sample would be split into two groups: experimental (A) and control (B).  For example, group 1 does ‘A’ then ‘B,’ and group 2 does ‘B’ then ‘A.’ This is to eliminate order effects.

Although order effects occur for each participant, they balance each other out in the results because they occur equally in both groups.

3. Matched Pairs Design

A matched pairs design is an experimental design where pairs of participants are matched in terms of key variables, such as age or socioeconomic status. One member of each pair is then placed into the experimental group and the other member into the control group .

One member of each matched pair must be randomly assigned to the experimental group and the other to the control group.

• Con : If one participant drops out, you lose 2 PPs’ data.
• Pro : Reduces participant variables because the researcher has tried to pair up the participants so that each condition has people with similar abilities and characteristics.
• Con : Very time-consuming trying to find closely matched pairs.
• Pro : It avoids order effects, so counterbalancing is not necessary.
• Con : Impossible to match people exactly unless they are identical twins!
• Control : Members of each pair should be randomly assigned to conditions. However, this does not solve all these problems.

Experimental design refers to how participants are allocated to an experiment’s different conditions (or IV levels). There are three types:

1. Independent measures / between-groups : Different participants are used in each condition of the independent variable.

2. Repeated measures /within groups : The same participants take part in each condition of the independent variable.

3. Matched pairs : Each condition uses different participants, but they are matched in terms of important characteristics, e.g., gender, age, intelligence, etc.

Learning Check

Read about each of the experiments below. For each experiment, identify (1) which experimental design was used; and (2) why the researcher might have used that design.

1 . To compare the effectiveness of two different types of therapy for depression, depressed patients were assigned to receive either cognitive therapy or behavior therapy for a 12-week period.

The researchers attempted to ensure that the patients in the two groups had similar severity of depressed symptoms by administering a standardized test of depression to each participant, then pairing them according to the severity of their symptoms.

2 . To assess the difference in reading comprehension between 7 and 9-year-olds, a researcher recruited each group from a local primary school. They were given the same passage of text to read and then asked a series of questions to assess their understanding.

3 . To assess the effectiveness of two different ways of teaching reading, a group of 5-year-olds was recruited from a primary school. Their level of reading ability was assessed, and then they were taught using scheme one for 20 weeks.

At the end of this period, their reading was reassessed, and a reading improvement score was calculated. They were then taught using scheme two for a further 20 weeks, and another reading improvement score for this period was calculated. The reading improvement scores for each child were then compared.

4 . To assess the effect of the organization on recall, a researcher randomly assigned student volunteers to two conditions.

Condition one attempted to recall a list of words that were organized into meaningful categories; condition two attempted to recall the same words, randomly grouped on the page.

Experiment Terminology

Ecological validity.

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables which are not independent variables but could affect the results (DV) of the experiment. Extraneous variables should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of taking part in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

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Statistics By Jim

Making statistics intuitive

Experimental Design: Definition and Types

By Jim Frost 3 Comments

What is Experimental Design?

An experimental design is a detailed plan for collecting and using data to identify causal relationships. Through careful planning, the design of experiments allows your data collection efforts to have a reasonable chance of detecting effects and testing hypotheses that answer your research questions.

An experiment is a data collection procedure that occurs in controlled conditions to identify and understand causal relationships between variables. Researchers can use many potential designs. The ultimate choice depends on their research question, resources, goals, and constraints. In some fields of study, researchers refer to experimental design as the design of experiments (DOE). Both terms are synonymous.

Ultimately, the design of experiments helps ensure that your procedures and data will evaluate your research question effectively. Without an experimental design, you might waste your efforts in a process that, for many potential reasons, can’t answer your research question. In short, it helps you trust your results.

Learn more about Independent and Dependent Variables .

Design of Experiments: Goals & Settings

Experiments occur in many settings, ranging from psychology, social sciences, medicine, physics, engineering, and industrial and service sectors. Typically, experimental goals are to discover a previously unknown effect , confirm a known effect, or test a hypothesis.

Effects represent causal relationships between variables. For example, in a medical experiment, does the new medicine cause an improvement in health outcomes? If so, the medicine has a causal effect on the outcome.

An experimental design’s focus depends on the subject area and can include the following goals:

• Understanding the relationships between variables.
• Identifying the variables that have the largest impact on the outcomes.
• Finding the input variable settings that produce an optimal result.

For example, psychologists have conducted experiments to understand how conformity affects decision-making. Sociologists have performed experiments to determine whether ethnicity affects the public reaction to staged bike thefts. These experiments map out the causal relationships between variables, and their primary goal is to understand the role of various factors.

Conversely, in a manufacturing environment, the researchers might use an experimental design to find the factors that most effectively improve their product’s strength, identify the optimal manufacturing settings, and do all that while accounting for various constraints. In short, a manufacturer’s goal is often to use experiments to improve their products cost-effectively.

In a medical experiment, the goal might be to quantify the medicine’s effect and find the optimum dosage.

Developing an Experimental Design

Developing an experimental design involves planning that maximizes the potential to collect data that is both trustworthy and able to detect causal relationships. Specifically, these studies aim to see effects when they exist in the population the researchers are studying, preferentially favor causal effects, isolate each factor’s true effect from potential confounders, and produce conclusions that you can generalize to the real world.

To accomplish these goals, experimental designs carefully manage data validity and reliability , and internal and external experimental validity. When your experiment is valid and reliable, you can expect your procedures and data to produce trustworthy results.

An excellent experimental design involves the following:

• Lots of preplanning.
• Developing experimental treatments.
• Determining how to assign subjects to treatment groups.

The remainder of this article focuses on how experimental designs incorporate these essential items to accomplish their research goals.

Learn more about Data Reliability vs. Validity and Internal and External Experimental Validity .

Preplanning, Defining, and Operationalizing for Design of Experiments

This phase of the design of experiments helps you identify critical variables, know how to measure them while ensuring reliability and validity, and understand the relationships between them. The review can also help you find ways to reduce sources of variability, which increases your ability to detect treatment effects. Notably, the literature review allows you to learn how similar studies designed their experiments and the challenges they faced.

Operationalizing a study involves taking your research question, using the background information you gathered, and formulating an actionable plan.

This process should produce a specific and testable hypothesis using data that you can reasonably collect given the resources available to the experiment.

• Null hypothesis : The jumping exercise intervention does not affect bone density.
• Alternative hypothesis : The jumping exercise intervention affects bone density.

To learn more about this early phase, read Five Steps for Conducting Scientific Studies with Statistical Analyses .

Formulating Treatments in Experimental Designs

In an experimental design, treatments are variables that the researchers control. They are the primary independent variables of interest. Researchers administer the treatment to the subjects or items in the experiment and want to know whether it causes changes in the outcome.

As the name implies, a treatment can be medical in nature, such as a new medicine or vaccine. But it’s a general term that applies to other things such as training programs, manufacturing settings, teaching methods, and types of fertilizers. I helped run an experiment where the treatment was a jumping exercise intervention that we hoped would increase bone density. All these treatment examples are things that potentially influence a measurable outcome.

Even when you know your treatment generally, you must carefully consider the amount. How large of a dose? If you’re comparing three different temperatures in a manufacturing process, how far apart are they? For my bone mineral density study, we had to determine how frequently the exercise sessions would occur and how long each lasted.

How you define the treatments in the design of experiments can affect your findings and the generalizability of your results.

Assigning Subjects to Experimental Groups

A crucial decision for all experimental designs is determining how researchers assign subjects to the experimental conditions—the treatment and control groups. The control group is often, but not always, the lack of a treatment. It serves as a basis for comparison by showing outcomes for subjects who don’t receive a treatment. Learn more about Control Groups .

How your experimental design assigns subjects to the groups affects how confident you can be that the findings represent true causal effects rather than mere correlation caused by confounders. Indeed, the assignment method influences how you control for confounding variables. This is the difference between correlation and causation .

Imagine a study finds that vitamin consumption correlates with better health outcomes. As a researcher, you want to be able to say that vitamin consumption causes the improvements. However, with the wrong experimental design, you might only be able to say there is an association. A confounder, and not the vitamins, might actually cause the health benefits.

Let’s explore some of the ways to assign subjects in design of experiments.

Completely Randomized Designs

A completely randomized experimental design randomly assigns all subjects to the treatment and control groups. You simply take each participant and use a random process to determine their group assignment. You can flip coins, roll a die, or use a computer. Randomized experiments must be prospective studies because they need to be able to control group assignment.

Random assignment in the design of experiments helps ensure that the groups are roughly equivalent at the beginning of the study. This equivalence at the start increases your confidence that any differences you see at the end were caused by the treatments. The randomization tends to equalize confounders between the experimental groups and, thereby, cancels out their effects, leaving only the treatment effects.

For example, in a vitamin study, the researchers can randomly assign participants to either the control or vitamin group. Because the groups are approximately equal when the experiment starts, if the health outcomes are different at the end of the study, the researchers can be confident that the vitamins caused those improvements.

Statisticians consider randomized experimental designs to be the best for identifying causal relationships.

If you can’t randomly assign subjects but want to draw causal conclusions about an intervention, consider using a quasi-experimental design .

Learn more about Randomized Controlled Trials and Random Assignment in Experiments .

Randomized Block Designs

Nuisance factors are variables that can affect the outcome, but they are not the researcher’s primary interest. Unfortunately, they can hide or distort the treatment results. When experimenters know about specific nuisance factors, they can use a randomized block design to minimize their impact.

This experimental design takes subjects with a shared “nuisance” characteristic and groups them into blocks. The participants in each block are then randomly assigned to the experimental groups. This process allows the experiment to control for known nuisance factors.

Blocking in the design of experiments reduces the impact of nuisance factors on experimental error. The analysis assesses the effects of the treatment within each block, which removes the variability between blocks. The result is that blocked experimental designs can reduce the impact of nuisance variables, increasing the ability to detect treatment effects accurately.

Suppose you’re testing various teaching methods. Because grade level likely affects educational outcomes, you might use grade level as a blocking factor. To use a randomized block design for this scenario, divide the participants by grade level and then randomly assign the members of each grade level to the experimental groups.

A standard guideline for an experimental design is to “Block what you can, randomize what you cannot.” Use blocking for a few primary nuisance factors. Then use random assignment to distribute the unblocked nuisance factors equally between the experimental conditions.

You can also use covariates to control nuisance factors. Learn about Covariates: Definition and Uses .

Observational Studies

In some experimental designs, randomly assigning subjects to the experimental conditions is impossible or unethical. The researchers simply can’t assign participants to the experimental groups. However, they can observe them in their natural groupings, measure the essential variables, and look for correlations. These observational studies are also known as quasi-experimental designs. Retrospective studies must be observational in nature because they look back at past events.

Imagine you’re studying the effects of depression on an activity. Clearly, you can’t randomly assign participants to the depression and control groups. But you can observe participants with and without depression and see how their task performance differs.

Observational studies let you perform research when you can’t control the treatment. However, quasi-experimental designs increase the problem of confounding variables. For this design of experiments, correlation does not necessarily imply causation. While special procedures can help control confounders in an observational study, you’re ultimately less confident that the results represent causal findings.

Learn more about Observational Studies .

For a good comparison, learn about the differences and tradeoffs between Observational Studies and Randomized Experiments .

Between-Subjects vs. Within-Subjects Experimental Designs

When you think of the design of experiments, you probably picture a treatment and control group. Researchers assign participants to only one of these groups, so each group contains entirely different subjects than the other groups. Analysts compare the groups at the end of the experiment. Statisticians refer to this method as a between-subjects, or independent measures, experimental design.

In a between-subjects design , you can have more than one treatment group, but each subject is exposed to only one condition, the control group or one of the treatment groups.

A potential downside to this approach is that differences between groups at the beginning can affect the results at the end. As you’ve read earlier, random assignment can reduce those differences, but it is imperfect. There will always be some variability between the groups.

In a  within-subjects experimental design , also known as repeated measures, subjects experience all treatment conditions and are measured for each. Each subject acts as their own control, which reduces variability and increases the statistical power to detect effects.

In this experimental design, you minimize pre-existing differences between the experimental conditions because they all contain the same subjects. However, the order of treatments can affect the results. Beware of practice and fatigue effects. Learn more about Repeated Measures Designs .

Design of Experiments Examples

For example, a bone density study has three experimental groups—a control group, a stretching exercise group, and a jumping exercise group.

In a between-subjects experimental design, scientists randomly assign each participant to one of the three groups.

In a within-subjects design, all subjects experience the three conditions sequentially while the researchers measure bone density repeatedly. The procedure can switch the order of treatments for the participants to help reduce order effects.

Matched Pairs Experimental Design

A matched pairs experimental design is a between-subjects study that uses pairs of similar subjects. Researchers use this approach to reduce pre-existing differences between experimental groups. It’s yet another design of experiments method for reducing sources of variability.

Researchers identify variables likely to affect the outcome, such as demographics. When they pick a subject with a set of characteristics, they try to locate another participant with similar attributes to create a matched pair. Scientists randomly assign one member of a pair to the treatment group and the other to the control group.

On the plus side, this process creates two similar groups, and it doesn’t create treatment order effects. While matched pairs do not produce the perfectly matched groups of a within-subjects design (which uses the same subjects in all conditions), it aims to reduce variability between groups relative to a between-subjects study.

On the downside, finding matched pairs is very time-consuming. Additionally, if one member of a matched pair drops out, the other subject must leave the study too.

Learn more about Matched Pairs Design: Uses & Examples .

Another consideration is whether you’ll use a cross-sectional design (one point in time) or use a longitudinal study to track changes over time .

A case study is a research method that often serves as a precursor to a more rigorous experimental design by identifying research questions, variables, and hypotheses to test. Learn more about What is a Case Study? Definition & Examples .

In conclusion, the design of experiments is extremely sensitive to subject area concerns and the time and resources available to the researchers. Developing a suitable experimental design requires balancing a multitude of considerations. A successful design is necessary to obtain trustworthy answers to your research question and to have a reasonable chance of detecting treatment effects when they exist.

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Dear Jim You wrote a superb document, I will use it in my Buistatistics course, along with your three books. Thank you very much! Miguel

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Thanks so much, Miguel! Glad this post was helpful and I trust the books will be as well.

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An Introduction to Experimental Design Research

• First Online: 18 May 2016

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• Philip Cash 4 ,
• Tino Stanković 5 &
• Mario Štorga 6  

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Design research brings together influences from the whole gamut of social, psychological, and more technical sciences to create a tradition of empirical study stretching back over 50 years (Horvath 2004 ; Cross 2007 ). A growing part of this empirical tradition is experimental, which has gained in importance as the field has matured. As in other evolving disciplines, e.g. behavioural psychology, this maturation brings with it ever-greater scientific and methodological demands (Reiser 1939 ; Dorst 2008 ). In particular, the experimental paradigm holds distinct and significant challenges for the modern design researcher. Thus, this book brings together leading researchers from across design research in order to provide the reader with a foundation in experimental design research; an appreciation of possible experimental perspectives; and insight into how experiments can be used to build robust and significant scientific knowledge. This chapter sets the stage for these discussions by introducing experimental design research, outlining the various types of experimental approach, and explaining the role of this book in the wider methodological context.

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15 Experimental Design Examples

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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Experimental design involves testing an independent variable against a dependent variable. It is a central feature of the scientific method .

A simple example of an experimental design is a clinical trial, where research participants are placed into control and treatment groups in order to determine the degree to which an intervention in the treatment group is effective.

There are three categories of experimental design . They are:

• Pre-Experimental Design: Testing the effects of the independent variable on a single participant or a small group of participants (e.g. a case study).
• Quasi-Experimental Design: Testing the effects of the independent variable on a group of participants who aren’t randomly assigned to treatment and control groups (e.g. purposive sampling).
• True Experimental Design: Testing the effects of the independent variable on a group of participants who are randomly assigned to treatment and control groups in order to infer causality (e.g. clinical trials).

A good research student can look at a design’s methodology and correctly categorize it. Below are some typical examples of experimental designs, with their type indicated.

Experimental Design Examples

The following are examples of experimental design (with their type indicated).

1. Action Research in the Classroom

Type: Pre-Experimental Design

A teacher wants to know if a small group activity will help students learn how to conduct a survey. So, they test the activity out on a few of their classes and make careful observations regarding the outcome.

The teacher might observe that the students respond well to the activity and seem to be learning the material quickly.

However, because there was no comparison group of students that learned how to do a survey with a different methodology, the teacher cannot be certain that the activity is actually the best method for teaching that subject.

2. Study on the Impact of an Advertisement

An advertising firm has assigned two of their best staff to develop a quirky ad about eating a brand’s new breakfast product.

The team puts together an unusual skit that involves characters enjoying the breakfast while engaged in silly gestures and zany background music. The ad agency doesn’t want to spend a great deal of money on the ad just yet, so the commercial is shot with a low budget. The firm then shows the ad to a small group of people just to see their reactions.

Afterwards they determine that the ad had a strong impact on viewers so they move forward with a much larger budget.

3. Case Study

A medical doctor has a hunch that an old treatment regimen might be effective in treating a rare illness.

The treatment has never been used in this manner before. So, the doctor applies the treatment to two of their patients with the illness. After several weeks, the results seem to indicate that the treatment is not causing any change in the illness. The doctor concludes that there is no need to continue the treatment or conduct a larger study with a control condition.

4. Fertilizer and Plant Growth Study

An agricultural farmer is exploring different combinations of nutrients on plant growth, so she does a small experiment.

Instead of spending a lot of time and money applying the different mixes to acres of land and waiting several months to see the results, she decides to apply the fertilizer to some small plants in the lab.

After several weeks, it appears that the plants are responding well. They are growing rapidly and producing dense branching. She shows the plants to her colleagues and they all agree that further testing is needed under better controlled conditions .

5. Mood States Study

A team of psychologists is interested in studying how mood affects altruistic behavior. They are undecided however, on how to put the research participants in a bad mood, so they try a few pilot studies out.

They try one suggestion and make a 3-minute video that shows sad scenes from famous heart-wrenching movies.

They then recruit a few people to watch the clips and measure their mood states afterwards.

The results indicate that people were put in a negative mood, but since there was no control group, the researchers cannot be 100% confident in the clip’s effectiveness.

6. Math Games and Learning Study

Type: Quasi-Experimental Design

Two teachers have developed a set of math games that they think will make learning math more enjoyable for their students. They decide to test out the games on their classes.

So, for two weeks, one teacher has all of her students play the math games. The other teacher uses the standard teaching techniques. At the end of the two weeks, all students take the same math test. The results indicate that students that played the math games did better on the test.

Although the teachers would like to say the games were the cause of the improved performance, they cannot be 100% sure because the study lacked random assignment . There are many other differences between the groups that played the games and those that did not.

Learn More: Random Assignment Examples

7. Economic Impact of Policy

An economic policy institute has decided to test the effectiveness of a new policy on the development of small business. The institute identifies two cities in a third-world country for testing.

The two cities are similar in terms of size, economic output, and other characteristics. The city in which the new policy was implemented showed a much higher growth of small businesses than the other city.

Although the two cities were similar in many ways, the researchers must be cautious in their conclusions. There may exist other differences between the two cities that effected small business growth other than the policy.

8. Parenting Styles and Academic Performance

Psychologists want to understand how parenting style affects children’s academic performance.

So, they identify a large group of parents that have one of four parenting styles: authoritarian, authoritative, permissive, or neglectful. The researchers then compare the grades of each group and discover that children raised with the authoritative parenting style had better grades than the other three groups. Although these results may seem convincing, it turns out that parents that use the authoritative parenting style also have higher SES class and can afford to provide their children with more intellectually enriching activities like summer STEAM camps.

9. Movies and Donations Study

Will the type of movie a person watches affect the likelihood that they donate to a charitable cause? To answer this question, a researcher decides to solicit donations at the exit point of a large theatre.

He chooses to study two types of movies: action-hero and murder mystery. After collecting donations for one month, he tallies the results. Patrons that watched the action-hero movie donated more than those that watched the murder mystery. Can you think of why these results could be due to something other than the movie?

10. Gender and Mindfulness Apps Study

Researchers decide to conduct a study on whether men or women benefit from mindfulness the most. So, they recruit office workers in large corporations at all levels of management.

Then, they divide the research sample up into males and females and ask the participants to use a mindfulness app once each day for at least 15 minutes.

At the end of three weeks, the researchers give all the participants a questionnaire that measures stress and also take swabs from their saliva to measure stress hormones.

The results indicate the women responded much better to the apps than males and showed lower stress levels on both measures.

Unfortunately, it is difficult to conclude that women respond to apps better than men because the researchers could not randomly assign participants to gender. This means that there may be extraneous variables that are causing the results.

11. Eyewitness Testimony Study

Type: True Experimental Design

To study the how leading questions on the memories of eyewitnesses leads to retroactive inference , Loftus and Palmer (1974) conducted a simple experiment consistent with true experimental design.

Research participants all watched the same short video of two cars having an accident. Each were randomly assigned to be asked either one of two versions of a question regarding the accident.

Half of the participants were asked the question “How fast were the two cars going when they smashed into each other?” and the other half were asked “How fast were the two cars going when they contacted each other?”

Participants’ estimates were affected by the wording of the question. Participants that responded to the question with the word “smashed” gave much higher estimates than participants that responded to the word “contacted.”

12. Sports Nutrition Bars Study

A company wants to test the effects of their sports nutrition bars. So, they recruited students on a college campus to participate in their study. The students were randomly assigned to either the treatment condition or control condition.

Participants in the treatment condition ate two nutrition bars. Participants in the control condition ate two similar looking bars that tasted nearly identical, but offered no nutritional value.

One hour after consuming the bars, participants ran on a treadmill at a moderate pace for 15 minutes. The researchers recorded their speed, breathing rates, and level of exhaustion.

The results indicated that participants that ate the nutrition bars ran faster, breathed more easily, and reported feeling less exhausted than participants that ate the non-nutritious bar.

13. Clinical Trials

Medical researchers often use true experiments to assess the effectiveness of various treatment regimens. For a simplified example: people from the population are randomly selected to participate in a study on the effects of a medication on heart disease.

Participants are randomly assigned to either receive the medication or nothing at all. Three months later, all participants are contacted and they are given a full battery of heart disease tests.

The results indicate that participants that received the medication had significantly lower levels of heart disease than participants that received no medication.

14. Leadership Training Study

A large corporation wants to improve the leadership skills of its mid-level managers. The HR department has developed two programs, one online and the other in-person in small classes.

HR randomly selects 120 employees to participate and then randomly assigned them to one of three conditions: one-third are assigned to the online program, one-third to the in-class version, and one-third are put on a waiting list.

The training lasts for 6 weeks and 4 months later, supervisors of the participants are asked to rate their staff in terms of leadership potential. The supervisors were not informed about which of their staff participated in the program.

The results indicated that the in-person participants received the highest ratings from their supervisors. The online class participants came in second, followed by those on the waiting list.

15. Reading Comprehension and Lighting Study

Different wavelengths of light may affect cognitive processing. To put this hypothesis to the test, a researcher randomly assigned students on a college campus to read a history chapter in one of three lighting conditions: natural sunlight, artificial yellow light, and standard fluorescent light.

At the end of the chapter all students took the same exam. The researcher then compared the scores on the exam for students in each condition. The results revealed that natural sunlight produced the best test scores, followed by yellow light and fluorescent light.

Therefore, the researcher concludes that natural sunlight improves reading comprehension.

See Also: Experimental Study vs Observational Study

Experimental design is a central feature of scientific research. When done using true experimental design, causality can be infered, which allows researchers to provide proof that an independent variable affects a dependent variable. This is necessary in just about every field of research, and especially in medical sciences.

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Experimental Research Design — 6 mistakes you should never make!

Since school days’ students perform scientific experiments that provide results that define and prove the laws and theorems in science. These experiments are laid on a strong foundation of experimental research designs.

An experimental research design helps researchers execute their research objectives with more clarity and transparency.

In this article, we will not only discuss the key aspects of experimental research designs but also the issues to avoid and problems to resolve while designing your research study.

Table of Contents

What Is Experimental Research Design?

Experimental research design is a framework of protocols and procedures created to conduct experimental research with a scientific approach using two sets of variables. Herein, the first set of variables acts as a constant, used to measure the differences of the second set. The best example of experimental research methods is quantitative research .

Experimental research helps a researcher gather the necessary data for making better research decisions and determining the facts of a research study.

When Can a Researcher Conduct Experimental Research?

A researcher can conduct experimental research in the following situations —

• When time is an important factor in establishing a relationship between the cause and effect.
• When there is an invariable or never-changing behavior between the cause and effect.
• Finally, when the researcher wishes to understand the importance of the cause and effect.

Importance of Experimental Research Design

To publish significant results, choosing a quality research design forms the foundation to build the research study. Moreover, effective research design helps establish quality decision-making procedures, structures the research to lead to easier data analysis, and addresses the main research question. Therefore, it is essential to cater undivided attention and time to create an experimental research design before beginning the practical experiment.

By creating a research design, a researcher is also giving oneself time to organize the research, set up relevant boundaries for the study, and increase the reliability of the results. Through all these efforts, one could also avoid inconclusive results. If any part of the research design is flawed, it will reflect on the quality of the results derived.

Types of Experimental Research Designs

Based on the methods used to collect data in experimental studies, the experimental research designs are of three primary types:

1. Pre-experimental Research Design

A research study could conduct pre-experimental research design when a group or many groups are under observation after implementing factors of cause and effect of the research. The pre-experimental design will help researchers understand whether further investigation is necessary for the groups under observation.

Pre-experimental research is of three types —

• One-shot Case Study Research Design
• One-group Pretest-posttest Research Design
• Static-group Comparison

2. True Experimental Research Design

A true experimental research design relies on statistical analysis to prove or disprove a researcher’s hypothesis. It is one of the most accurate forms of research because it provides specific scientific evidence. Furthermore, out of all the types of experimental designs, only a true experimental design can establish a cause-effect relationship within a group. However, in a true experiment, a researcher must satisfy these three factors —

• There is a control group that is not subjected to changes and an experimental group that will experience the changed variables
• A variable that can be manipulated by the researcher
• Random distribution of the variables

This type of experimental research is commonly observed in the physical sciences.

3. Quasi-experimental Research Design

The word “Quasi” means similarity. A quasi-experimental design is similar to a true experimental design. However, the difference between the two is the assignment of the control group. In this research design, an independent variable is manipulated, but the participants of a group are not randomly assigned. This type of research design is used in field settings where random assignment is either irrelevant or not required.

The classification of the research subjects, conditions, or groups determines the type of research design to be used.

Advantages of Experimental Research

Experimental research allows you to test your idea in a controlled environment before taking the research to clinical trials. Moreover, it provides the best method to test your theory because of the following advantages:

• Researchers have firm control over variables to obtain results.
• The subject does not impact the effectiveness of experimental research. Anyone can implement it for research purposes.
• The results are specific.
• Post results analysis, research findings from the same dataset can be repurposed for similar research ideas.
• Researchers can identify the cause and effect of the hypothesis and further analyze this relationship to determine in-depth ideas.
• Experimental research makes an ideal starting point. The collected data could be used as a foundation to build new research ideas for further studies.

6 Mistakes to Avoid While Designing Your Research

There is no order to this list, and any one of these issues can seriously compromise the quality of your research. You could refer to the list as a checklist of what to avoid while designing your research.

1. Invalid Theoretical Framework

Usually, researchers miss out on checking if their hypothesis is logical to be tested. If your research design does not have basic assumptions or postulates, then it is fundamentally flawed and you need to rework on your research framework.

2. Inadequate Literature Study

Without a comprehensive research literature review , it is difficult to identify and fill the knowledge and information gaps. Furthermore, you need to clearly state how your research will contribute to the research field, either by adding value to the pertinent literature or challenging previous findings and assumptions.

3. Insufficient or Incorrect Statistical Analysis

Statistical results are one of the most trusted scientific evidence. The ultimate goal of a research experiment is to gain valid and sustainable evidence. Therefore, incorrect statistical analysis could affect the quality of any quantitative research.

4. Undefined Research Problem

This is one of the most basic aspects of research design. The research problem statement must be clear and to do that, you must set the framework for the development of research questions that address the core problems.

5. Research Limitations

Every study has some type of limitations . You should anticipate and incorporate those limitations into your conclusion, as well as the basic research design. Include a statement in your manuscript about any perceived limitations, and how you considered them while designing your experiment and drawing the conclusion.

6. Ethical Implications

The most important yet less talked about topic is the ethical issue. Your research design must include ways to minimize any risk for your participants and also address the research problem or question at hand. If you cannot manage the ethical norms along with your research study, your research objectives and validity could be questioned.

Experimental Research Design Example

In an experimental design, a researcher gathers plant samples and then randomly assigns half the samples to photosynthesize in sunlight and the other half to be kept in a dark box without sunlight, while controlling all the other variables (nutrients, water, soil, etc.)

By comparing their outcomes in biochemical tests, the researcher can confirm that the changes in the plants were due to the sunlight and not the other variables.

Experimental research is often the final form of a study conducted in the research process which is considered to provide conclusive and specific results. But it is not meant for every research. It involves a lot of resources, time, and money and is not easy to conduct, unless a foundation of research is built. Yet it is widely used in research institutes and commercial industries, for its most conclusive results in the scientific approach.

Have you worked on research designs? How was your experience creating an experimental design? What difficulties did you face? Do write to us or comment below and share your insights on experimental research designs!

Frequently Asked Questions

Randomization is important in an experimental research because it ensures unbiased results of the experiment. It also measures the cause-effect relationship on a particular group of interest.

Experimental research design lay the foundation of a research and structures the research to establish quality decision making process.

There are 3 types of experimental research designs. These are pre-experimental research design, true experimental research design, and quasi experimental research design.

The difference between an experimental and a quasi-experimental design are: 1. The assignment of the control group in quasi experimental research is non-random, unlike true experimental design, which is randomly assigned. 2. Experimental research group always has a control group; on the other hand, it may not be always present in quasi experimental research.

Experimental research establishes a cause-effect relationship by testing a theory or hypothesis using experimental groups or control variables. In contrast, descriptive research describes a study or a topic by defining the variables under it and answering the questions related to the same.

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Science for Everyone

Science education, teaching resources, scientific literacy, and more!

• Feb 27, 2023
• 37 min read

100+ labs, activities, and science experiments for middle and high school students

Updated: Sep 12, 2023

Looking for fun and engaging lab ideas for use in your science class? You've come to the right place. Here's my list of practical activities and experiments you can try with your students, all in one place.

Introduction

I've been teaching MYP science (grades 6-10) internationally for over a decade. I try to devote about a third of my class time to experimental activities, which means we do a lot of labs. I'm also the only lab technician at my current school, so I'm responsible for set-up and cleanup as well. Needless to say, I've accumulated quite a bit of experience in the lab and can confidently recommend all of the following experiments. I have personally tried all of them with my students and in most cases have been conducting them for years.

Where can I get these labs?

Although I have provided links to just about every activity on my list, some are much better than others. You will often need to adjust them significantly for your classroom depending on the classes and number of students you teach, as well as the materials you have access to. Additionally, free lab guides usually do not include much in the way of background info, student questions or handouts, and teacher prep notes. For these reasons, I've created my own resources for most of the labs I do, which I'm currently in the process of uploading to my TPT store . You can access my complete experimental resource collection here . I'm adding new experiments regularly, but early purchasers will have access to all future activities at no additional cost! As you can see from the list below, I have quite a few more to add! Links to my individual lab activities have also been provided in the relevant sections.

The following list of experiments is organized by grade and unit. Click on the links below to jump to the section you need.

Scientific Method

Chemical and physical changes, characteristics of living things, earth and space science, objects in motion, atoms and elements, inheritance, earth systems and cycles, energy, work, and power, human impacts on the environment, electromagnetism, communication, body systems, organization of life, chemical reactions and bonding, forces and structures, pure substances and mixtures, health and disease, electricity, environmental chemistry, space exploration, organic chemistry, genetics and reproduction, radiation and radioactivity.

You can also access unit plan outlines for each of the above units in my store .

List of Experiments

1. Memory experiments - How many random words or objects can students memorize? Will students be able to identify a missing object (or classmate)? How reliable is eyewitness testimony? These are excellent activities for getting students to practice basic experimental design, as well as practical skills like collecting and analyzing data. As a bonus, many of these experiments require virtually no prep for you!

2. Accuracy and precision experiment - For this activity, all you need is a target and something that will stick to it. I've used a dart board, NERF guns with suction cup darts, or just a target drawn on the whiteboard with magnetic disks to throw at it. You could even make a target on the ground outside and have your students toss beanbags at it. Anything will do! Students take turns hurling things at the targets and recording the distance to the middle. Then they analyze the results in terms of accuracy (average distance to the center) and precision (range, standard deviation, and the number of decimal places).

3. Baking science experiment - I've long been a fan of incorporating food science into science class. What better way to get kids excited about chemistry while also learning a useful life skill? I've done many versions of this activity, but it always involves baking something, like cookies or cakes, by modifying a recipe to learn about variables. There are also many opportunities here for unit conversion practice.

4. Fire triangle oxygen experiment - For younger students who may not have had access to open flames before, this is a good place to start for developing safe handling practices. Students use candles and various glass containers to measure how long a flame will burn in a limited oxygen environment. You might as well demonstrate the rising water trick too since you're using all the same materials.

5. Bunsen burner temperature experiment - Students learn how the Bunsen burner works and attempt to find out what part of the flame is hottest. You'll need a temperature probe rated for high temperatures in order to get accurate data for this lab. Alternatively, you can just use a thin steel rod or a nail by holding it in different parts of the flame and recording any colour changes. Try comparing the orange and blue flames, as well as the top, bottom, middle, and sides of the flame. Almost all students fail to predict where the hottest region will be!

6. Fire extinguisher safety activity - You'll need to get some safety approvals for this one, but I think it's worth it. Who knows when you might have a real emergency to deal with? Go outside and start a simple fire in a safe place like a metal tray. Then use one of the school fire extinguishers to put it out. Have a few students try it, too. Discuss the locations of fire extinguishers, different types of fires, exit strategies, and so on. You may even want to coordinate with the local fire department and see if they can send someone to talk to the students about fire safety.

7. Lab equipment identification quiz - Another one for younger students who are just beginning to do lab work. Collect one piece of glassware or lab equipment for each student in your class (with a few extra, just in case). Put one on each desk before your students arrive. As students come in, give them a blank piece of paper and have them write down the name of the equipment in front of them. Then rotate to the next seat and repeat. Set a timer for ~30 seconds to keep things moving. You may also want to get students to draw the equipment as well, in which case they would need a bit of extra time. This works best as a review activity, but it can also be used as a chance to see what students already know.

8. Oobleck states of matter activity - After students have learned about solids, liquids, and gases, whip up a batch of oobleck (cornstarch + water) and have them explore its properties. Is it a solid, a liquid, or a bit of both? Have your students consider the particle interactions going on in this unusual fluid. A bit of research may be required on their part.

9. Salt or sugar crystal lab - Students dissolve and then evaporate a very small volume of concentrated salt or sugar solution to produce crystals. I prefer salt since it is less of a sticky mess, but you can do one or both. Check out the crystals under the microscope and compare them to mineral crystals if you have some to observe. You can also use this lab as a chance to explore saturation and give a really cool supersaturation demo using sodium acetate. Just Google 'hot ice'!

10. Separating salt and sand mixtures experiment - Give students a sample containing salt, sand, and (optionally) iron filings. Then tell them to figure out how to separate each substance. This will involve a combination of magnetism, dissolving, filtration, evaporation, and so on. There is more than one way to accomplish this task, which is what makes it interesting. Add to the challenge by having students compare the mass of their sample to the total mass of each separated substance (you'll need to allow time for proper drying) to see who managed to preserve the most material. Discuss industrial and everyday applications of these separation techniques.

11. Diffusion of potassium permanganate - Another classic chemistry experiment that involves the movement of purple crystals as they dissolve in water. You can have students record the time it takes to produce a uniform solution and then compare this at different water temperatures or volumes. It's also useful to get your students to take photos, or better yet, videos of the process to compare.

12. Dissolving cocoa experiment - Students try to find the best way to dissolve a specific quantity of cocoa in order to make hot chocolate effectively. This is great for winter or during the holiday season, but as far as I'm concerned there's never a bad time for hot chocolate. Make this as open-ended as you can, but have students write down their reasoning and the method used. Is it better to use milk or water? Should you add the liquid first or the powder first? How does the temperature affect how easily the cocoa dissolves? Obviously, you wouldn't want to use typical chemistry glassware for this one, so plan ahead if you want to allow your students to drink their creations.

13. Pond organisms microscope lab - Visit a local ecosystem and collect some water. It's better to get the 'chunky stuff' including mud, water plants, pond scum, and other organic material. Bring it back to the lab and have your students look for microorganisms under the microscope. With luck, you will be able to identify water fleas, vorticella, euglena, and other organisms in your samples. If you do this in the spring or summer, various insect larvae will likely be present as well.

14. Specialized cells microscope lab - Sure, you can easily find prepared slides of specialized cells, but it's much more interesting for your students if they create their own. Collecting cheek cells is simple, and I think students get a kick out of seeing their own cells for the first time. Root hair cells and leaf cells are easy to locate as well, although the species you choose makes a huge difference. Try a few and see what works best.

15. Energy in food (calorimetry) experiment - I've found this lab to be notoriously difficult unless you have very good equipment, but it has the potential to produce lots of interesting results and is endlessly customizable for your students. The flexibility alone makes it worthwhile to try, even if the results don't end up being as useful as you'd hoped. Burning sugary or oily foods works best. Try potato chips or marshmallows.

16. Comparing fuels experiment - In this lab activity, students burn equal quantities of various fuels to determine which makes the best fuel. Alcohols including methanol, ethanol, and propanol should be easy enough to obtain. The experiment itself is pretty straightforward, but there are a lot of factors to consider. Which fuel burns the hottest? Which burns the longest? Which is the cheapest or easiest to obtain? What about other factors, such as the smell? There's a lot for students to explore here.

17. Design a solar oven - This is an excellent project for students to tackle that requires only a few simple craft and household supplies. As a summative assessment, students can make use of their physics knowledge to conduct, reflect, insulate, and ultimately cook simple foods. I prefer not to tell them the exact method so that they can find out what works and what doesn't. We like to make s'mores and cheese toast - things that are still delicious even if they don't cook properly!

18. Thermal conductivity experiment - This lab has many variations, but most involve comparing the ability of different metals to transfer heat. If you can find wires of equal diameter and length made of copper, steel, etc. then it is pretty easy to compare the conductivity of these substances by putting one end in hot water and then measuring the temperature along its length using a digital thermometer. Alternatively, put the other end of the wire in cold water and measure the temperature after a given amount of time. The warmest liquid should indicate the best conductor.

19. Insulated drink experiment - This is a bit like the solar oven experiment, except that in this case, students are given a hot beverage and tasked with keeping it warm for as long as possible. Similar ideas and materials can be used, so it makes sense to do this as a follow-up experiment to that. Provide each group with a cup of boiled water and identical materials in order to make it a fair comparison. Then pop a thermometer in and see who has the warmest drink by the end of class.

20. Mark-recapture simulation - This mathematical exercise involves students estimating a population's size by 'marking' and 'capturing' beans or other small objects from a container. Various sample sizes are used to show how accuracy improves with the number of marked and captured individuals. Then discuss how accurate mark-recapture studies would be for different populations in the wild.

21. Quadrat study - Using square frames, students collect data on the plant or invertebrate species found in a local environment (the school playground will do!). They can use this information to estimate population sizes and species distribution, but it can also be used to identify possible community interactions, including competition, mutualism, commensalism, and predation.

22. Mesocosm experiment - Students set up small ecosystems in jars or soda bottles to observe nutrient cycling in action. If done well they can last for years. I've tried aquatic ecosystems with fish in the past, but for ethical reasons, we pretty much stick to plants and soil organisms only these days. I also like to keep it simple and do everything in large peanut butter jars. It can get pretty elaborate if you decide to make full eco-columns though.

23. Personal impact experiment - This is an open-ended investigation where students decide on a lifestyle change they will maintain for a few weeks in order to reduce their environmental impact. This could include things like reducing shower time, air drying their clothes, biking to school instead of getting a ride, and so on. They then attempt to estimate the impact they are making in terms of environmental and economic savings, both for the project duration and for a lifetime, if they were to keep it up indefinitely. Although most students quickly fall back into their regular routines, a few do recognize that small changes are manageable and decide to make compromises in the way they live.

24. Solar system scale model - This is a mathematical/visual investigation that shows kids the true scale of the solar system (it's mostly empty space!). We usually do this on two different scales - one that allows us to fit the solar system within the classroom, and another that requires us to go outside and cover some distance on the playground. Even at that scale, the largest planets are still only the size of a small ball and the Earth is minuscule.

25. Chocolate rock cycle - The rock cycle can be a bit dull and abstract considering the time scales and forces students are expected to imagine. Spice it up a little with the delicious addition of chocolate! Through mixing, grating, melting, hardening, and other processes, you can mimic most of the changes in the rock cycle and give students a clearer understanding of igneous, sedimentary, and metamorphic rock formation. Yum!

26. Rock and mineral identification with dichotomous keys - Once students have a grasp of the rock cycle, it's time to get them familiar with some of the more common rocks and minerals. This can be done outdoors with field guides and cameras, or inside with samples and a dichotomous key. Even with a key this can be pretty challenging and there is quite a bit of terminology to understand, but I still think it's worthwhile. Some kids get really into it!

27. Flashlight moon phases activity - With a couple of balls, a flashlight, and a darkened room, you can put small groups of students to work trying to simulate the movements of the Sun, Earth, and Moon as they orbit around each other. It is pretty funny watching kids trying to move everything correctly and position themselves to see the phases properly, but I think it gets the concepts across quite well. You can also simulate eclipses, and if you're feeling really ambitious, you can get Mars involved and demonstrate retrograde motion.

28. Human body systems of measurement activity - I use this exercise to teach students about units and what they are based on. In ancient times, a lot of measurement standards were based on the distances between or across body parts, including the hand, fingers, and arms. Most of the metric units in use today are based on much more complicated standards, but it can be surprising for students to realize that measuring devices can't really be made without standards of some kind.

29. Comparing ancient and modern maps - In the age of discovery (~1400-1600) a lot of early mapping was accomplished by Europeans. It wasn't all accurate, however, for a variety of reasons. This activity gives students a good crash course in cartography, which you might argue is more appropriate for geography and social studies, but it works well for this unit because it devotes a significant amount of time to understanding how we determine our location in space. Graphing using GPS coordinates is a significant part of this investigation.

30. GPS pathways activity - Since practically all of your students have access to an accurate GPS device of their own, this once-expensive activity is now easier than ever. You can use one of a handful of apps to track students as they walk various pathways around the schoolyard and elsewhere, which can then be analyzed and compared in terms of distance, time, acceleration, and changes in elevation. It can be particularly fun to compare the pathways students take to get to school. I couldn't find a good link for this activity, unfortunately.

31. Determining the acceleration of gravity using a pendulum - This lab always produces consistent results, provided students perform the calculations correctly. Since we use the acceleration of gravity so often in physics, I think it helps students to see that it can be determined with a relatively simple setup. Students will have only tiny pendulums to work with at their desks, but if you plan ahead, you can make a huge one that hangs from the ceiling to show that it works at larger scales, too.

32. Metals and non-metals identification - Depending on what materials you have available, this can be a really great introduction to the periodic table of elements. Chances are your lab already has a good selection of metals, as well as some non-metals like carbon and sulfur. Provide small samples of each material and allow students to rotate to different stations where they attempt to identify the materials using some simple techniques (like magnetism). You can do this entirely visually if you like, or give some obscure facts about each element to help them out.

33. Metal displacement reactions experiment - Students observe whether reactions occur between pure metals and various salt solutions in order to create a simple reactivity series. It's reasonable to test four or five metals in order to introduce the concept, but you'll need to explain that very reactive and unreactive metals are not realistic to test in school due to prohibitive costs and safety concerns. The reactivity series can then be used to discuss why certain metals are chosen for particular uses and why precious metals are so valuable and long-lasting.

34. pH indicator lab - There are many kinds of indicators that can be used to determine pH. Teach students about acids and alkalis, then give them a selection of common household substances like vinegar and soapy water to test with universal indicator. They can then use the results to create their own coloured pH scale in their notebooks and label each substance accordingly. If you want to go a little further, consider making your own indicator solution with purple cabbage!

35. Titration of NaOH with HCl - This classic chemistry experiment involves the neutralization reaction between hydrochloric acid and sodium hydroxide. A small sample of NaOH containing the indicator phenolphthalein is given to each group (this is best done in pairs). Students slowly add acid to the pink alkali solution until it goes clear, at which point it should be neutral. If you have digital pH probes, get students to measure the changes throughout the experiment and plot them on a graph. You can also evaporate the resulting solution to show that salt (NaCl) crystals are formed in the process.

36. Fruit DNA extraction - In this simple lab activity, students use common household ingredients to isolate and observe the DNA found in fruits such as kiwis, bananas, and strawberries. The similarities should help illustrate the fact that DNA is a universal code common to all organisms. Although you can't see the molecular structure of the DNA in this exercise, it's still fun to have a look at your extracted nucleic acids under a microscope.

37. Life cycles investigation - There are a few ways to show students how various plants and animals complete their life cycles. For plants, beans grow quite quickly and the seeds are easy to collect and save. If you have some space and a bit more time, sunflowers are really fun to grow at school, too. Depending on the season, it may be possible to collect some tadpoles from a local pond and observe them as they grow and develop. Insect larvae and caterpillars are fun to watch as well, but a little less exciting until their final metamorphosis. You might even be able to contact a nearby chicken farm and get ahold of some fertilized eggs to incubate. Be sure to provide food and a decent living space for whatever creatures you investigate, of course, and have a plan for what to do with them after you are done observing them.

38. Phenotype investigation - Collect class data for a variety of common genetic traits and compare these to national or global averages. Blood type is ideal, but some students might not know theirs (everyone knows their blood type in Japan so this is an easy one for me!). Other possibilities include eye colour (here's a VERY detailed article on eye colour genetics ), dominant hand, hair colour, or earlobe shape. Avoid things like height, which might single out or embarrass some students. Also, don't do tongue rolling, because despite what you may have heard, the ability to roll your tongue is either mostly or entirely NOT determined by genes .

39. Determining the speed of sound experiment - This one requires some space, and by space, I mean distance. You'll want at least 200m with a clear line of sight for good results. Have a few students position themselves at 100m increments away from a group of observers (the rest of the class). These students will be equipped with some kind of noise-making device that can also serve as a visual cue. We use two blocks of wood clapped together above the head. The observers use a stopwatch to measure the time between when they see the blocks touch and when they hear the sound. This is then used to calculate the speed of sound. If you get really lucky on a stormy day, you can do a variation of this exercise using lightning (from indoors, of course). In that case, your students would be finding the distance of the lightning using an accepted speed of sound.

40. Create a pinhole camera - It's not really an experiment, but it's still a classic physics exercise and for good reason. Nothing more clearly illustrates the function of the eye and retina and the concept of light moving in straight lines than this ancient device. A small cardboard box or similar container forms the basis for the pinhole camera, along with a few other craft materials. Then look at a bright object like a lightbulb or candle to see the inverted image.

41. Hearing or colour sensitivity experiment - For this activity, students will use different videos or apps to determine how well they can differentiate between similar shades of a colour or hear high-pitched sounds. Have the whole class perform the tests and then analyze the results. Students love learning about themselves!

42. Reflection investigations - Using lasers and different types of mirrors, students observe the behaviour of light and construct ray diagrams. This is good practice for drawing clear and detailed diagrams. This can take a while, so it's probably best to split it into two lessons and keep plane and curved mirrors separate.

43. Playing a song with glasses of water - This is a pretty silly activity that I decided to do a few years ago, but there's some solid science behind it. Students can obviously make music on glasses of water without learning anything, so be sure to indicate (and possibly calculate) how and why the pitch changes with volume. Put on a concert at the end of class and get your cameras ready!

44. Water cycle simulation - With just a few simple materials you can easily show many of the processes involved in the water cycle. Get a fish tank and fill it with a small amount of water. Place some sand or a rock on one side to represent mountains. Cover the tank with a clear sheet of glass or plastic so you can still see what's going on. Put a tray of ice above the mountain to represent cold air in the upper atmosphere. Finally, place a heat lamp near the tank to represent the sun (you can also just use the actual sun!). A 'cloud' should form below the ice with lots of condensation which will drip down the mountains and back into the 'ocean'. If you want to speed up the process, try adding warm water to the tank.

45. Weather comparison investigation - Students look up weather information for a number of different cities and record things like temperature, wind speed, humidity, pressure, and so on for a week or two. Then they analyze the data and prepare a report or presentation on the similarities and differences between the chosen locations. They must use their knowledge of air and ocean currents, elevation, latitude, and other factors to explain any observed differences. This works best as a summative assessment comparing where you live to a selection of other cities chosen by the students themselves.

46. Plate tectonics simulation - I've tried several different materials to simulate the movements of tectonic plates, and each has its pros and cons. Slowly pushing crackers over a peanut butter or jam 'mantle' until they collide is pretty fun and shows some fault interactions reasonably well. I also like smashing layers of towels or paper into each other to show how mountains and unusual strata patterns can form. You can also try freezing the top few centimeters of a large container of water (just leave it outside if it's cold enough in winter!). Use a hammer to smash the surface and form a few 'plates'. Then move them across the surface to show plate movements and interactions. You can also use this to illustrate how the continents were once connected as a single land mass.

47. Human sense perception lab - This is one of my personal favourites. Students move around in pairs visiting a variety of stations that put their senses of hearing, touch, taste, sight, and smell to the test. In total students do 13 interactive sense activities that are easy to set up and fun to experience.

48. Plant tropism experiments - Use a fast-growing plant like beans to show how plants grow towards the light (phototropism) and away from gravity (gravitropism). Students can get a bit creative with this one by coming up with modifications to test. Some possibilities include growing a plant sideways or upside down, rotating a plant away from the sun every few days, or covering different parts of a growing shoot with various materials to see how phototropism is controlled.

49. Invertebrate stimuli and response experiments - Unlike mammals, invertebrates such as insects, worms, and snails exhibit mostly predictable responses to specific stimuli. Go outside and collect whatever tiny creatures you can from your local ecosystem. Then bring them back to the lab to test their responses to things like temperature gradients, moisture, light, sound, movement, and so on. Use this opportunity to discuss the ethical treatment of laboratory animals and make it clear that your students must do their best to avoid harming the creatures in their care. Release them where you found them after the experiments are done!

50. Human power experiment - Get your students running up flights of stairs to see how much power a human can generate! I like to kick off this activity by discussing horsepower and its origins as a unit of measurement. Inevitably students want to see how they compare to a horse (and each other), so this always gets competitive. Spoiler alert - your students can't beat a horse! To end the activity, we discuss situations in which a horse can be defeated by a human, focusing on an annual marathon that pits the two species against each other held in the UK. As it turns out, humans perform best when it's hot.

51. Impact crater experiment - This is good messy fun with applied physics! Students drop marbles into trays of flour from different heights and compare the diameter and depth of the impact craters. Use marbles with different masses and calculate the potential energy for each trial. This should equal the kinetic energy on impact. How does the kinetic energy affect crater size and depth? Graph it and find out!

52. Principle of moments lab - Students use a balance beam to solve problems and investigate the principle of moments. This is the idea that when two opposing turning forces act equally on either side of a pivot, they are balanced and no movement occurs. I like this activity because it can be completed in a number of ways, including trial and error, calculation, or a combination of both.

53. Gear ratios experiment - You'll need access to some specific equipment for this one. A bicycle should be easy enough to obtain (or borrow), but I use LEGO technic . Students build simple LEGO cars and switch out different gears to compare the force and speed produced. This takes a while and is definitely more complicated than just demonstrating with a stationary bicycle or similar setup, but it's far more hands-on. This is best for smaller classes or science clubs.

54. Pulley experiment - Here's another activity that requires you to have some materials on hand, although they can be easily purchased from a local hardware store for a reasonable price. Give students an object of known mass and a force meter. Then give them the pulley materials and set them to work on reducing the force needed to lift the mass by as much as possible. If they have already studied the theory behind pulleys, this should be doable, but the reality of setting up a working block and tackle is much more complicated than it seems if you've never done it before.

55. Greenhouse effect simulation - Using sealed jars or other containers, students modify atmospheric conditions to see how temperature is affected. There are many ways to conduct this experiment, so I recommend letting each group of students try something different. Start with a simple control (usually an empty container) and go from there. You can try adding different quantities of water, soil, or ice, or if you can get your hands on it, chunks of dry ice to increase the CO2 concentration.

56. Chemical tests for macromolecules - This is a well-documented set of biochemistry procedures for identifying starch, simple sugars, proteins, and fats in small samples of common foods. You only need a few reagents which should be readily available in most middle or high school science labs. The results involve various colour changes that are pretty fun to observe. Get your students to predict which foods will contain each macromolecule and then test their predictions to see if they are right.

57. Plant nutrient deficiencies investigation - Teach your students how to recognize signs of nutrient deficiencies in plants. These can be seen by examining leaf growth and colour. Then go outside and see if your students can find examples of nutrient-deficient plants around the school. Beware of plants that are naturally red/purple in colour, which could be mistaken for phosphorous deficiencies, and definitely don't try this in the fall for obvious reasons.

58. Digestion simulation - In this activity, students take a sample of food and put it through a series of processes in order to simulate the stages of digestion. This doesn't sound that fun, but it involves a lot of smashing, squishing, and messy fun. Crush the food inside a plastic bag, add some water and hydrochloric acid, filter it through a pair of socks or stocking 'intestines', and then compact the leftover chunks to make 'poop' (kids love it). Combine this activity with the chemical tests from experiment 56 above for a more in-depth analysis. I like using cornflakes or a similar cereal as the carbs are pretty easy to break down and there is enough iron for you to actually extract and see.

59. Digestive system dissection - If your students aren't too squeamish, consider dissecting something to view its digestive system. Whole fish are easy to obtain here in Japan so that's what I use. Rather than opting for a class set, if you splurge for a big one, you can cut open the stomach and examine the contents. I did this several times with carp in college and there was always lots to see. If you happen to know a hunter, you might be able to get something much larger. Enjoy!

60. Protease enzyme experiment - Some fruits, such as pineapples, naturally contain protein-digesting enzymes. If you attempt to make gelatin with a sufficient quantity of these fruits, it won't solidify. Try a bunch of different fruits and see which ones contain enzymes and which don't. Make sure you use fresh fruits as canned varieties can be unreliable. Then discuss enzyme activity and its importance in digestive processes.

61. Cell respiration experiment - Use germinating beans or yeast to indirectly measure the rate of cell respiration at different temperatures. You can accomplish this by placing these organisms inside a sealed system called a respirometer and measuring their CO2 production. It's a little complicated to set up for students, but you can prepare some of the materials ahead of time yourself. This experiment encourages accurate measurement techniques and can be used on invertebrates as well!

62. Magnetic field investigation - Put a magnet on a piece of paper and sprinkle with iron filings. Then draw the resulting magnetic field lines. Try different magnet shapes, or add multiple magnets in different arrangements to see how the magnetic field changes. If you have clear sheets of plexiglass or even just a blank laminated sheet you can put the magnets underneath and make cleanup MUCH easier.

63. Make a compass activity - Float a magnetized needle on water and watch it point north. Everyone has probably done this one at some point, but for younger students, this is still a tried and true way to observe the Earth's magnetic field. Apparently, you can also just suspend a bar magnet on a string and accomplish the same thing, but I've never tried that. Might be worth a go!

64. Fruit and vegetable battery experiment - Use a lemon or potato to generate electricity and power a simple device like a fan or light bulb. That's the basic version, anyway. You can make it more of an experiment by comparing how pH affects the voltage produced or by adjusting the distance between the electrodes. Obviously, you can also compare different fruits and vegetables to see which works the best. Some of them might surprise you (try a pumpkin!).

65. Series and parallel circuits investigation - Build different kinds of circuits and compare the voltage and current at different points. You'll need quite a few materials for a full class activity, including batteries, components, and a lot of wire, so consider doing this as part of a station activity if supplies are limited. You can also get kits that simplify and streamline the building process, but I like making students do it the hard way!

66. Electrical conductivity experiment - Compare the resistance of various materials using a multimeter. It's as simple as it sounds and generates really good data. The hardest part is finding similar materials for a fair test, as your wires need to be the same length and diameter if you are comparing different metals. One version of this activity involves using graphite from pencil drawings to compare conductivity. Simply draw two large dots on paper and connect them with a line. Then measure by placing your multimeter on the dots. Try making long or short lines, waves, or other shapes. Increase the thickness of your lines to see if that makes a difference.

67. Total internal reflection experiment - Shine a laser into a semi-circular transparent block at different angles until it reflects back rather than refracting through. You can use a small clear container or fish tank as well. Get students to use a protractor to find the critical angle, which can be calculated and compared for that substance as well. There are better ways to observe total internal reflection, including streaming water and the use of fiber optics, but those work better as demos.

68. Audio format sound quality experiment - Convert lossless quality music files to MP3s at different bit rates and see if your students can hear the differences in quality. You can do this with a free music editor such as Audacity . It's harder than you think, even with good headphones. If you have no idea what I'm talking about, read the linked article about file formats and audio quality. Compare WAV files to 320 kbps VBR MP3s and 192 kbps CBR MP3s. Let your students choose their favourite songs if you like, or give them some music education by choosing yours!

69. Wi-fi signal strength experiment - See how different variables affect the signal strength of wi-fi signals, such as the distance, number of connected devices, or physical obstructions. Download a free signal strength app that measures in dBm to compare signals and collect data. Since this is a logarithmic scale you can take the opportunity to teach students about that, too. This is a super practical experiment that students find quite relevant to their needs.

70. Sheep/pig brain dissection - Another dissection, this time with a medium-sized brain. In order to get the most out of this lab, it's best to frontload a lot of the terminology and be sure students are familiar with the main brain regions and their functions. I like to get kids to follow along with a video dissection, pausing as needed, or just use a document camera to guide them through it yourself if you're confident to do so. One word of wisdom - don't freeze your brains prior to dissection. I do this with hearts and thought it would be fine, but nope! Brains turn to mush when you thaw them out.

71. Properties of bone experiment - Cook and soak bones in acid to remove the substances that give them strength. Chicken bones are the easiest to obtain, especially if you plan to have enough for a full class (there might be a wing night or two in your future!). You can combine this lab with a microscope investigation of bone tissue, or look at cross-sections of bones from avian and non-avian species (images are fine).

72. Antagonistic muscle groups activity - Construct a model of an antagonistic muscle group (the biceps/triceps arm pair is almost always used) and observe how the bones and muscles work during flexion and contraction. There are many different materials that can be used to make this work. It can be as simple as popsicle sticks and elastic bands, or more complicated models using wood or PVC to represent the bones and stretch cords or balloons for the muscles.

73. Observing body tissues microscope lab - Students look at a variety of human tissues under the microscope and attempt to identify them. It's not that hard to create mounts of different plant tissues, but animal tissues are a lot less practical. For these, I like to get a good set of prepared slides and have students do an ID quiz by rotating around the room. I'll usually give them a list of possible tissue types to choose from, but I don't always teach them what to look for ahead of time. The reasoning behind their choices is usually very good and worth writing down.

74. Microscope / biological drawings lab - In this lab, we revisit some properties of cells from earlier courses and refresh the students' memories on proper microscope use. In the process I have them create very detailed microscope drawings of protists - usually paramecium at 400x. The goal is to draw for accuracy and scale. I also need to regularly emphasize that students draw what they see, not what they expect to see.

75. Organs diagram activity - Students are given cutouts of human organs and have to place them in their correct positions on a blank torso. You can do this at the beginning or end of a topic (or both!), but either way, it's pretty hilarious to observe at times. After students are fairly confident with their choices, I have them label and annotate their diagrams explaining what each organ does. Finally, we look at the actual diagram and make corrections where necessary (I usually use a student exemplar from someone who knows what they're doing!). I've also done a whole class version of this exercise where I draw a life-sized torso on the whiteboard and have students take turns placing organs on it. There's a lot of communication from the 'audience' and it's always entertaining.

75. Dichotomous key activity - Students create a dichotomous key to differentiate and identify a selection of everyday objects. Sure, you could do this with images of actual species, in which case I would stick with a group of closely related organisms (turtles, sharks, cats, bears, etc.), however, I find that this is much more engaging when done with objects that have nothing to do with biology. Save yourself time and money by using whatever you have on hand, like candies, school supplies, or weird and random objects from your 'junk' drawer!

76. Flame test lab - Always a student favourite, this lab involves burning small quantities of metal salts to produce coloured flames. These can be used to identify specific metals. It is also the basis for the colours seen in fireworks. While that connection is easy to make, it's much harder for students to understand why each metal produces a different colour, but this is actually a good opportunity to introduce electron configurations. Let your students use their phones for this lab and they'll enjoy taking lots of cool photos and videos.

77. Properties of ionic and covalent compounds experiment - There are lots of ways to do this (and lots of compounds to test), but the simplest one I know of is to compare salt (sodium chloride), sugar (sucrose), and paraffin wax. Students can examine a number of properties, including melting point and conductivity, to determine the typical features of ionic and covalent substances. A much more interesting version of this experiment involves giving students a bunch of unknown substances and asking them to determine whether they are ionic or covalent. This is most easily accomplished by testing for conductivity, but let them figure that out for themselves!

78. Properties of metals lab - Students test a bunch of common metals to investigate their properties. This can include both a qualitative (describe the colour and other physical features) and quantitative analysis (measuring the conductivity, density, and so on). I find it best if you can get equally sized samples of each metal, whether that be cubes, wires, or strips. I usually have students fill out a table of all the properties. I also include a few rare metals (like gold, platinum, iridium, etc.) that they have to research and add to the table themselves).

79. Electroplating experiment - Students use a zinc solution and electricity to coat a copper plate with a thin layer of zinc. I make this a seasonal activity by drawing holiday-themed designs on the copper using a permanent marker. When removed, it creates a nice contrast between the dull grey zinc and shiny orange copper metals. Heat it lightly in a Bunsen burner and you'll create brass instead. Then punch a hole in the top and you've got a unique ornament for your Christmas tree.

80. Preventing rust experiment - Take a selection of iron nails and coat them with different protective substances before placing them in water. Leave them in there for a few days to see how much they rust. The goal is to learn about the factors that contribute to corrosion and to see if we can prevent it with readily available materials. You can also do a follow-up experiment where you use things like acid to remove the layer of rust.

81. Newton's 3rd law skateboard experiment - There are lots of versions of this, but I like to get kids up and moving a bit using rollerblades or skateboards if possible (safety first!). Get a student to sit on the skateboard and then toss a medicine ball. Measure how far they threw the ball and how far they rolled, and then repeat this with different masses of medicine balls (and students!). Do lots of trials to smooth out inconsistent data. If you don't have skateboards you can always just use balloon-powered rockets or cars to show the same concepts.

82. Hooke's law elastic spring constant experiment - With nothing more than a few small weights and an elastic band, you can investigate Hooke's law. This is so simple and quick that I would recommend doing it more than once with either different elastics or springs . As the name implies, springs produce better data, but you might not have enough for a full class, and once they're stretched out, that's pretty much the end of them.

83. Center of gravity experiment - Students try to find an accurate way of locating the center of gravity for irregularly shaped objects. I don't tell them how to do it at first to see if anyone can come up with a decent method. We might even test a few ideas if they seem reasonable, but otherwise, we'll go to the prescribed lab to complete the activity. All you need is a thick paper cut into irregular shapes. I use pieces of cardboard or old greeting cards.

84. Engineering challenges activity - Create and test towers and bridges using craft materials or building sets, then test them for strength and efficiency. Normally I do this at the end of our unit on forces and structures, but this year we did it twice - once at the beginning and then again after they had learned some more about construction and engineering. The results were greatly improved! My resources for this particular experiment are freely available here , by the way.

85. Types of mixtures lab - This is another great example of an experiment that makes use of what you have, saving you time and hopefully a shopping trip. Students mix household solids and liquids to explore different types of homogeneous and heterogeneous mixtures. Use small quantities as this lab can generate a lot of waste that needs to be cleaned up. You can also illustrate the Tyndall effect by shining a flashlight through your mixtures to see if they scatter the light.

86. Dialysis or potato osmosis experiment - Use dialysis tubing to illustrate the concept of osmosis, typically with sugar, salt, or starch solutions of varying concentrations. Unfortunately, I rarely have dialysis tubing on hand, so we do the potato version with saltwater instead. I've been keeping our data for many years, however, so we compare and combine the data to arrive at much better results. My big breakthrough with this method has been to use cookie cutters when preparing the potato samples. It makes the sizes and surface areas much more consistent, although you still have to weigh each sample. 24 hours is a perfect amount of time to leave your samples in solution.

87. Molecular gastronomy spherification lab - Students make bubble tea using fruit juice. This is a fairly challenging experiment, but the result is often worth the effort (plus, you can eat it). Molecular gastronomy has a lot more to explore and honestly I wish I knew more about it. I've always wanted to try an activity to have students create and taste unusual flavour combinations based on similar chemical compounds, but I've never gotten around to it.

88. Chromatography crime lab - Separate the pigments in marker ink using various solvents in order to solve a 'crime'. You can go full CSI on this one if you're inspired to do so, and I think kids appreciate it. The experiment itself is not that thrilling, so you really have to play up the forensics angle and focus on the practical aspects of the technique.

89. Infection simulation - Give each student a solution that represents their body fluids. One student is 'infected' with a different solution. Students mingle around sharing fluids for a few rounds before testing the liquids to see who else is now infected. The cups containing the infected solution turn bright pink when phenolphthalein indicator is added. This is one of my favourite activities for exploring immunity, vaccination, and viruses. It also lends itself very well to discussions on STIs.

90. Graveyard survivorship and life expectancy investigation - Visit a local graveyard and collect as much data as you can. Then analyze it over the next couple of lessons. This is very location dependent, but luckily you can access similar records online using sites like Find A Grave . I still think going to the actual site where people are buried is much more meaningful, but it works either way. Use the results to create survivorship curves, compare life expectancy over time, and look for specific birth and death events. Try doing this as an interdisciplinary activity with social studies!

91. Electron flow student simulation - Kids act out an electrical circuit by pretending to be electrons. It sounds stupid, but it works beautifully and even older students usually come around once they try it. Increase the voltage by having students move faster. Add components and batteries using chairs or levels to show gaining and losing energy. Add switches to stop and start the flow of students. Then create a series or parallel circuit and get students to adjust their movements accordingly. Surprisingly I couldn't find a decent online version of this, so I guess I need to upload mine soon!

92. Fuse wire experiment - Use fuse wires of variable thickness to explore the relationship between current and resistance. You'll need a low-voltage power supply and the wires themselves, as well as other standard electrical circuit materials. This has the potential to be slightly dangerous, so be sure to prepare students adequately beforehand and make safety requirements clear.

93. Water quality investigation - There are many aspects of water that can be tested in schools, including hardness, pH, and the presence of nitrates. Purchase water quality testing kits to save yourself a ton of effort. I send each kid home with a small container and tell them to get some water. Most kids will bring their tap water, but some get creative and scoop a sample out of a puddle, pond, stream, or toilet (ew...). Label everything accordingly and then start analyzing those samples! I really love showing the film Erin Brockovich in combination with this activity as it's directly related and based on a true story.

94. Acid rain plant germination and growth experiment - Various concentrations of acidified water are used on germinating seeds and healthy plants to observe the effects on their growth and general health. You can use vinegar as the experiment here suggests, but I make a more realistic batch of 'acid rain' by combining nitric and sulfuric acids in order to get a pH below 5. We then dilute this solution as necessary to use on our plants. I like to use radishes since they grow so quickly and require very little space/depth. You can grow them right in the classroom with a few trays or planters.

95. Natural selection simulation - Students act as predators to capture prey using a variety of utensils. Both predator and prey populations change with each 'generation', but only the strongest survive! I really like this activity for introducing or reviewing the concept of natural selection, and it pairs well with the board game Evolution .

96. Hominid migration mapping activity - This website is awful, but the activity is great. Students use hominid fossil data to plot locations on a map and then suggest migration routes our ancestors might have taken. There is one typo in the data but I always forget to write down which one it is. Don't worry, your students will find it! When finished you can refer them to this updated interactive which helps explain human migration patterns.

97. Kepler's laws investigation - This is really a collection of experiments to explore the laws of planetary motion outlined by Johannes Kepler about 400 years ago. Students will be drawing and performing calculations related to ellipses and learning about centripetal force. There's a decent amount of geometry and other maths involved, so you might want to coordinate with the math department if that's something that interests you.

98. Lenses investigation - Using a series of lasers and lenses, students refract light and produce images on a screen to find focal lengths. This can be difficult without the proper equipment so I think it's worth investing in a couple of decent optics sets for your lab. Treat them well and they should last practically forever.

99. Eye dissection - Cow eyes are typically used for this investigation of eye structure and function. It's simple enough, but I haven't done it in years since it's hard to get the materials where I live. If you want to avoid the mess, there are many virtual options or videos you can use instead.

100. Doppler effect experiment - A simple smartphone app is used to explore the way motion affects sound frequencies. The experiment itself is pretty easy to do, but it's important to then link these concepts back to light and the expanding universe, which is responsible for the red-shift observed with very distant objects.

101. Night sky investigation - Another set of app investigations that use star-gazing software on your phone or tablet to explore the locations and movements of celestial objects. The good thing about these apps is that you can complete them in broad daylight, but I still think it's worth organizing a star-gazing event at night with telescopes so that students can view actual planets and moons. If you aren't confident running this yourself (I'm not), ask a physics teacher or contact your local astronomy club/observatory.

102. DIY Spectrometer experiment - Got a pile of useless CDs? Use them to create your own spectrometer to observe the spectral lines from different light sources. There are far more complicated designs available online if you are a tinkerer, but I find that this one works well enough for our purposes. There are some apps that will analyze a photo of spectral lines and suggest what elements might be present, but there isn't a single one that I would really recommend at this time. Explore what's available on your device as apps are constantly changing.

103. Molecular modeling activity - I'll take any excuse to get out the modeling kits. For this activity, students practice making various organic functional groups. I sometimes give each group equal components and see what kinds of different isomers they can come up with. It's easy to get caught up with complicated naming procedures in organic chemistry so I find that this hands-on activity helps to put some of the theory in perspective without being too demanding. If you want to challenge your students on a rainy afternoon (or whenever) give them something really big to make, like a phospholipid, or a section of DNA.

104. Esters investigation - Making esters by combining carboxylic acids with alcohols is relatively straightforward, if you have access to the necessary reactants. Students get to smell a bunch of things and relate this to compounds found naturally in foods and those artificially added to perfumes and other products. This lab makes use of concentrated sulfuric acid, which only you should handle for safety reasons.

105. Polymer slime activity - Making slime is fun for all ages, but you might wonder why I do it with grade 10 as it's more of an elementary school type of activity. Although younger students love to make and play with slime, they can't really appreciate the chemistry behind it, so that's why I toss this in at the end of a tough organic chemistry unit. You don't have to make the usual borax / PVA slime, but I find that it is simple and flexible enough to illustrate all of the concepts I want to hit. We also use this as a jumping-off point to discuss the use of plastics and other polymers as well as their effects on the environment.

106. Identifying plastics lab - Not all plastics are created equal in terms of their suitability for recycling. In this experiment, students will use the density of various plastic samples to identify them. We often watch a documentary associated with this experiment, such as Plastic Problem or Plastic Wars , both from PBS.

107. PCR and gel electrophoresis experiment - If your school has a PCR and electrophoresis machine, you're good to go and can begin examining samples of DNA without much effort, but if you don't, you'll need to connect with another organization that does. In the past, I've taken students to local universities in order to make use of their equipment, which they are usually happy to share for educational purposes. Sometimes they even prepare the lesson for us!

108. Flower anatomy investigation - If you plan to teach this unit in the warmer months you'll have access to all kinds of flowers you can dissect and examine. If you want to leave them on the plants you can just take photos I guess, but you'll miss out on exploring what's inside. Large flowers such as lilies are among the best and most straightforward examples of flower anatomy, but be sure to have a look at other flower types, including composites like sunflowers to see if your students can still identify all of the structures. Don't just look at flower structure, however. Make sure you discuss the functions of each part, and more importantly, why each species has evolved in a particular way. This is a good chance to talk about pollination as well.

109. Radioactive decay simulation - This is a statistics activity using dice to determine radioactive decay events. The experiment involves graphing, half-life calculations, and discussions on nuclear waste, so it's a well-rounded activity for any unit on radiation.

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Experimental Design Activities

These series of activities are designed for students to collaborate together to come up with something unique and interesting. For many of the experimental questions, students will work in pairs or groups to come up with their own experimental design. If the school has resources and there is enough time, students can test their designs. If students are not able to make and test their designs, they can draw and explain what their ideas are to the class or hand it in if you wish to look at their ideas. Students are encouraged to use the scientific method when coming up with their experimental designs.

See how Sheldon and Leonard use the scientific method:

Download the full document with descriptions here: Experimental Design Activities

Experiment 1: Egg Drop

The Scientific Method: obtained from www.sciencebuddies.org

Experimental question: What attire will you design in order to prevent an egg from breaking if dropped from a 2-3 story building?

Questions to consider:

• What materials will you use?
• How will you ensure the egg does not break if dropped off a roof top?
• How will you test and re-test your design?

Ask students to write their thought process in terms of the scientific method (research, hypothesis, test hypothesis, re-evaluate, conclude).

Students should lay out exactly how they plan to test their design. Students should also draw a picture of their design and mention why they think it will be so effective.

Material limitations:

• 6 cotton balls
• 3 ft of tape
• 2 sheets of paper towel
• 4 popsicle sticks
• How will you use the materials?
• What design will allow you to use the materials most effectively and wisely?
• How will you re-test if the first design is not successful?

Note: many of these materials are very inexpensive and the school may have them. If you wish, you might be able to come prepared with a bin of materials and have students test out some designs.

Take a look at how the Mythbusters take on the egg drop challenge!

Experiment 2: Mold Test

Experimental question: What conditions are best for keeping food fresh?

Design an experiment to show which conditions are best for keeping bread fresh. (ie. does bread stay fresher in cold temperatures, darkness, moist environments, etc.)

• What will your control be?
• What are your independent and dependent variable?
• How many different conditions will you test? What will these variables be?
• From experience, what do you notice about the freshness of your food? Where is it stored?

Download the full version below!

Experiment 3: Cargo Ship

Experimental question: What design of boat will hold the most amount of pennies?

Design a style of boat using limited materials that will hold the moat amount of pennies when placed in water.

• One sheet of aluminum foil (approx 12 inches long and 6 inches wide)
• 1ft of tape
• 6 popsicle sticks
• 2 small birthday candles (optional)

Note: you may alter the materials as necessary, adding more or removing, or changing the current materials listed

Instructions:

• Students get into groups of 4 or 5
• Students use materials to design a boat that will hold as many pennies as possible without sinking
• Students can use as little or as much of the provided material as needed
• Give students 30 minutes to design their boat
• Have groups test their designs against other groups – fill up a sink with water, place two boats in the sink or water bath, place one penny at a time in the boat until one of them finally sinks

Download: Experimental Design Activities

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8.1 Experimental design: What is it and when should it be used?

Learning objectives.

• Define experiment
• Identify the core features of true experimental designs
• Describe the difference between an experimental group and a control group
• Identify and describe the various types of true experimental designs

Experiments are an excellent data collection strategy for social workers wishing to observe the effects of a clinical intervention or social welfare program. Understanding what experiments are and how they are conducted is useful for all social scientists, whether they actually plan to use this methodology or simply aim to understand findings from experimental studies. An experiment is a method of data collection designed to test hypotheses under controlled conditions. In social scientific research, the term experiment has a precise meaning and should not be used to describe all research methodologies.

Experiments have a long and important history in social science. Behaviorists such as John Watson, B. F. Skinner, Ivan Pavlov, and Albert Bandura used experimental design to demonstrate the various types of conditioning. Using strictly controlled environments, behaviorists were able to isolate a single stimulus as the cause of measurable differences in behavior or physiological responses. The foundations of social learning theory and behavior modification are found in experimental research projects. Moreover, behaviorist experiments brought psychology and social science away from the abstract world of Freudian analysis and towards empirical inquiry, grounded in real-world observations and objectively-defined variables. Experiments are used at all levels of social work inquiry, including agency-based experiments that test therapeutic interventions and policy experiments that test new programs.

Several kinds of experimental designs exist. In general, designs considered to be true experiments contain three basic key features:

• random assignment of participants into experimental and control groups
• a “treatment” (or intervention) provided to the experimental group
• measurement of the effects of the treatment in a post-test administered to both groups

Some true experiments are more complex.  Their designs can also include a pre-test and can have more than two groups, but these are the minimum requirements for a design to be a true experiment.

Experimental and control groups

In a true experiment, the effect of an intervention is tested by comparing two groups: one that is exposed to the intervention (the experimental group , also known as the treatment group) and another that does not receive the intervention (the control group ). Importantly, participants in a true experiment need to be randomly assigned to either the control or experimental groups. Random assignment uses a random number generator or some other random process to assign people into experimental and control groups. Random assignment is important in experimental research because it helps to ensure that the experimental group and control group are comparable and that any differences between the experimental and control groups are due to random chance. We will address more of the logic behind random assignment in the next section.

Treatment or intervention

In an experiment, the independent variable is receiving the intervention being tested—for example, a therapeutic technique, prevention program, or access to some service or support. It is less common in of social work research, but social science research may also have a stimulus, rather than an intervention as the independent variable. For example, an electric shock or a reading about death might be used as a stimulus to provoke a response.

In some cases, it may be immoral to withhold treatment completely from a control group within an experiment. If you recruited two groups of people with severe addiction and only provided treatment to one group, the other group would likely suffer. For these cases, researchers use a control group that receives “treatment as usual.” Experimenters must clearly define what treatment as usual means. For example, a standard treatment in substance abuse recovery is attending Alcoholics Anonymous or Narcotics Anonymous meetings. A substance abuse researcher conducting an experiment may use twelve-step programs in their control group and use their experimental intervention in the experimental group. The results would show whether the experimental intervention worked better than normal treatment, which is useful information.

The dependent variable is usually the intended effect the researcher wants the intervention to have. If the researcher is testing a new therapy for individuals with binge eating disorder, their dependent variable may be the number of binge eating episodes a participant reports. The researcher likely expects her intervention to decrease the number of binge eating episodes reported by participants. Thus, she must, at a minimum, measure the number of episodes that occur after the intervention, which is the post-test .  In a classic experimental design, participants are also given a pretest to measure the dependent variable before the experimental treatment begins.

Types of experimental design

Let’s put these concepts in chronological order so we can better understand how an experiment runs from start to finish. Once you’ve collected your sample, you’ll need to randomly assign your participants to the experimental group and control group. In a common type of experimental design, you will then give both groups your pretest, which measures your dependent variable, to see what your participants are like before you start your intervention. Next, you will provide your intervention, or independent variable, to your experimental group, but not to your control group. Many interventions last a few weeks or months to complete, particularly therapeutic treatments. Finally, you will administer your post-test to both groups to observe any changes in your dependent variable. What we’ve just described is known as the classical experimental design and is the simplest type of true experimental design. All of the designs we review in this section are variations on this approach. Figure 8.1 visually represents these steps.

An interesting example of experimental research can be found in Shannon K. McCoy and Brenda Major’s (2003) study of people’s perceptions of prejudice. In one portion of this multifaceted study, all participants were given a pretest to assess their levels of depression. No significant differences in depression were found between the experimental and control groups during the pretest. Participants in the experimental group were then asked to read an article suggesting that prejudice against their own racial group is severe and pervasive, while participants in the control group were asked to read an article suggesting that prejudice against a racial group other than their own is severe and pervasive. Clearly, these were not meant to be interventions or treatments to help depression, but were stimuli designed to elicit changes in people’s depression levels. Upon measuring depression scores during the post-test period, the researchers discovered that those who had received the experimental stimulus (the article citing prejudice against their same racial group) reported greater depression than those in the control group. This is just one of many examples of social scientific experimental research.

In addition to classic experimental design, there are two other ways of designing experiments that are considered to fall within the purview of “true” experiments (Babbie, 2010; Campbell & Stanley, 1963).  The posttest-only control group design is almost the same as classic experimental design, except it does not use a pretest. Researchers who use posttest-only designs want to eliminate testing effects , in which participants’ scores on a measure change because they have already been exposed to it. If you took multiple SAT or ACT practice exams before you took the real one you sent to colleges, you’ve taken advantage of testing effects to get a better score. Considering the previous example on racism and depression, participants who are given a pretest about depression before being exposed to the stimulus would likely assume that the intervention is designed to address depression. That knowledge could cause them to answer differently on the post-test than they otherwise would. In theory, as long as the control and experimental groups have been determined randomly and are therefore comparable, no pretest is needed. However, most researchers prefer to use pretests in case randomization did not result in equivalent groups and to help assess change over time within both the experimental and control groups.

Researchers wishing to account for testing effects but also gather pretest data can use a Solomon four-group design. In the Solomon four-group design , the researcher uses four groups. Two groups are treated as they would be in a classic experiment—pretest, experimental group intervention, and post-test. The other two groups do not receive the pretest, though one receives the intervention. All groups are given the post-test. Table 8.1 illustrates the features of each of the four groups in the Solomon four-group design. By having one set of experimental and control groups that complete the pretest (Groups 1 and 2) and another set that does not complete the pretest (Groups 3 and 4), researchers using the Solomon four-group design can account for testing effects in their analysis.

Solomon four-group designs are challenging to implement in the real world because they are time- and resource-intensive. Researchers must recruit enough participants to create four groups and implement interventions in two of them.

Overall, true experimental designs are sometimes difficult to implement in a real-world practice environment. It may be impossible to withhold treatment from a control group or randomly assign participants in a study. In these cases, pre-experimental and quasi-experimental designs–which we  will discuss in the next section–can be used.  However, the differences in rigor from true experimental designs leave their conclusions more open to critique.

Experimental design in macro-level research

You can imagine that social work researchers may be limited in their ability to use random assignment when examining the effects of governmental policy on individuals.  For example, it is unlikely that a researcher could randomly assign some states to implement decriminalization of recreational marijuana and some states not to in order to assess the effects of the policy change.  There are, however, important examples of policy experiments that use random assignment, including the Oregon Medicaid experiment. In the Oregon Medicaid experiment, the wait list for Oregon was so long, state officials conducted a lottery to see who from the wait list would receive Medicaid (Baicker et al., 2013).  Researchers used the lottery as a natural experiment that included random assignment. People selected to be a part of Medicaid were the experimental group and those on the wait list were in the control group. There are some practical complications macro-level experiments, just as with other experiments.  For example, the ethical concern with using people on a wait list as a control group exists in macro-level research just as it does in micro-level research.

Key Takeaways

• True experimental designs require random assignment.
• Control groups do not receive an intervention, and experimental groups receive an intervention.
• The basic components of a true experiment include a pretest, posttest, control group, and experimental group.
• Testing effects may cause researchers to use variations on the classic experimental design.
• Classic experimental design- uses random assignment, an experimental and control group, as well as pre- and posttesting
• Control group- the group in an experiment that does not receive the intervention
• Experiment- a method of data collection designed to test hypotheses under controlled conditions
• Experimental group- the group in an experiment that receives the intervention
• Posttest- a measurement taken after the intervention
• Posttest-only control group design- a type of experimental design that uses random assignment, and an experimental and control group, but does not use a pretest
• Pretest- a measurement taken prior to the intervention
• Random assignment-using a random process to assign people into experimental and control groups
• Solomon four-group design- uses random assignment, two experimental and two control groups, pretests for half of the groups, and posttests for all
• Testing effects- when a participant’s scores on a measure change because they have already been exposed to it
• True experiments- a group of experimental designs that contain independent and dependent variables, pretesting and post testing, and experimental and control groups

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Exploring Experimental Design: Using hands-on activities to learn about experimental design

Science and Children—January 1999

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101 Ways to Design an Experiment, or Some Ideas About Teaching Design of Experiments by William G. Hunter

williamghunter.net > Articles > 101 Ways to Design an Experiment

I want to share some ideas about teaching design of experiments. They are related to something I have often wondered about: whether it is possible to let students experience first-hand all the steps involved in an experimental investigation-thinking of the problem, deciding what experiments might shed light on the problem, planning the runs to be made, carrying them out, analyzing the results, and writing a report summarizing the work. One curiosity about most courses on experimental designing, it seems to me, is that students get no practice designing realistic experiments although, from homework assignments, they do get practice analyzing data. Clearly, however, because of limitations of time and money, if students are to design experiments and actually carry them out, they cannot be involved with elaborate investigations. Therefore, the key question is this: Is it feasible for students to devise their own simple experiments and carry them through to completion and, if so, is it of any educational value to have them do so? I believe the answer to both parts of the question is yes, and the purpose of this paper is to explain why.

The particular design course I have taught most often is a one-semester course that includes these standard statistical techniques: t-tests (paired and unpaired), analysis of variance (primarily for one-way and two-way layouts), factorial and fractional factorial designs (emphasis given to two-level designs), the method of least squares (for linear and nonlinear models), and response surface methodology. The value of randomization and blocking is stressed. Special attention is given to these questions: What are the assumptions being made? What if they are violated? What common pitfalls are encountered in practice? What precautions can be taken to avoid these pitfalls? In analyzing data how can one determine whether the model is adequate? Homework problems provide ample opportunity for carefully examining residuals, especially by plotting them. The material for this course is discussed in the context of the iterative nature of experimental investigations.

Most of those who have taken this course have been graduate students, principally in engineering (chemical, civil, mechanical, industrial, agricultural) but also in a variety of other fields including statistics, food science, forestry, chemistry, and biology. There is a prerequisite of a one-semester introductory statistics course, but this requirement is customarily waived for graduate students with the understanding that they do a little extra work to catch up.

Simulated Data

One possibility is to use simulated data, and the scope here is wide, especially with the availability of computers. At times I have given assignments of this kind, especially response surface problems. Each student receives his or her own sets of data based upon the designs he or she chooses.

The problem might be set up as one involving a chemist who wishes to find the best settings of these five variables-temperature, concentration, pH, stirring rate, and amount of catalyst-and to determine the local geography of the response surface(s) near the optimum. To define the region of operability, ranges are specified for each of these variables. Perhaps more than one response can be measured, for instance, yield and cost. The student is given a certain budget, either in terms of runs or money, the latter being appropriate if there is an option provided for different types of experiments which have different costs. The student can ask for data in, say, three stages. Between these stages the accumulated data can be analyzed so that future experiments can be planned on the basis of all available information.

In generating the data, which contains experimental error, there are many possibilities. Different models can be used for each student, the models not necessarily being the usual simple first-order or second-order linear models. Not all variables need to be important, that is, some may be dummy variables (different ones for different students). Time trends and other abnormalities can be deliberately introduced into the data provided to the students.

The student prepares a report including a summary of the most important facts discovered about his or her system and perhaps containing a contour map of the response surface(s) for the two most important variables (if three of the five variables are dummies, this map should correspond to the true surface from which the data were generated). It is instructive then to compare each student's findings with the corresponding true situation.

Students enjoy games of this type and learn a considerable amount from them. For many it is the first time they realize just how frustrating the presence of an appreciable amount of experimental error can be. The typical prearranged undergraduate laboratory experiments in physics and chemistry, of course, have all important known sources of experimental error removed (typically the data are supposed to fall on a straight line-exactly-or else).

One's first reaction might be that there are not enough possibilities for experiments of this kind. But this is incorrect, as is illustrated by Table 1, which lists some of the experiments reported by the students. Experiments number 1-63 are of the home type and experiments number 64-101 are of the laboratory type. Note the variety of studies done. To save space, for most variables the levels used are not given. Anyway, they are not essential for our purposes here. Most of these experiments were factorial designs. Let us look briefly at the first two home experiments and the first two laboratory experiments.

Bicycle Experiment

In experiment number 1 the student, Norman Miller, using a factorial design with all points replicated, studied the effects of three variables-seat height (26, 30 inches), light generator (on or off), and tire pressure (40, 55 psi)-on two responses-time required to ride his bicycle over a particular course and his pulse rate at the finish of each run (pulse rate at the start was virtually constant). To him the most surprising result was how much he was slowed down by having the generator on. The average time for each run was approximately 50 seconds. He discovered that raising the seat reduced the time by about 10 seconds, having the generator on increased it by about one-third that amount and inflating the tires to 55 psi reduced the time by about the same amount that the generator increased it. He planned further experiments.

Popcorn Experiment

In experiment number 2 the student, Karen Vlasek, using a factorial design with four replicated center points, determined the effects of three variables on the amount of popcorn produced. She found, for example, that although double the yield was obtained with the gourmet popcorn, it cost three times as much as the regular popcorn. By using this experimental design she discovered approximately what combination of variables gave her best results. She noted that it differed from those recommended by the manufacturer of her popcorn popper and both suppliers of popcorn.

Dilution experiment

In experiment number 64 the student, Dean Hafeman, studied a routine laboratory procedure (a dilution) that was performed many times each day where he worked-almost on a mass production basis. The manufacturer of the equipment used for this work emphasized that the key operations, the raising and lowering of two plungers, had to be done slowly for good results. The student wondered what difference it would make if these operations were done quickly. He set up a factorial design in which the variables were the raising and lowering of plunger A and the raising and lowering of plunger B. The two levels of each variable were slow and fast. To his surprise, he found that none of the variables had any measurable effect on the readings. This conclusion had important practical implications in his laboratory because it meant that good results could be obtained even if the plungers were moved quickly; consequently a considerable amount of time could be saved in doing this routing work.

Trouble-shooting Experiment

In experiment number 65 the student, Rodger Melton, solved a trouble-shooting problem that he encountered in his research work. In one piece of his apparatus an extremely small quantity of a certain chemical was distilled to be collected in a second piece of the apparatus. Unfortunately, some of this material condensed prematurely in the line between these two pieces of apparatus. Was there a way to prevent this? By using a factorial design the problem was solved, it being discovered that by suitably adjusting the voltage and using a J-tube none of the material condensed prematurely. The column temperature, which was discovered to be minor consequence as far as premature condensation was concerned (a surprise), could be set to maximize throughput.

Most Popular Experiments

The most popular home experiments have concerned cooking since recipes lend themselves so readily to variations. What to measure for the response has sometimes created a problem. Usually a quality characteristic such as taste has been determined (preferably independently by a number of judges) on a 1-5 or 1-10 scale. Growing seeds has also been an easy and popular experiment. In the laboratory experiments, sensitivity or robustness tests have been the most common (the dilution experiment, number 65, discussed above is of this type). Typically the experimenter varies the conditions for a standard analytical procedure (for example, for the measurement of chemical oxygen demand, COD) to see how much the measured value is affected. That is, if the standard procedure calls for the addition of 20 ml. of a particular chemical, 18 ml. and 22 ml. might be tried. Results from such tests are revealing no matter which way they turn out. One student, for example, concluded ``The results sort of speak for themselves. The test is not very robust.'' Another student, who studied a different test, reported ``The results of the Yates analysis show that the COD test is indeed robust.''

Structuring the Assignment

I have always made these assignments completely open, saying that they could study anything that interested them. I have tended to favor home rather than laboratory experiments. I have suggested they choose something they care about, preferably something they've wondered about. Such projects seem to turn out better than those picked for no particularly good reason. Here is how a few of the reports began: ``Ever since we came to Madison my family has experienced difficulty in making bread that will rise properly.'' ``Since moving to Madison, my green thumb has turned black. Every plant I have tried to grow has died.'' (Nothing works in Madison?) ``This experiment deals with how best to prepare pancakes to satisfy the group of four of us living together.'' ``I rent an efficiency on the second floor of an apartment building which has cooking facilities on the first floor only. When I cook rice, my staple food,I have to make one to three visits to the kitchen to make sure it is ready to be served and not burned. Because of this inconvenience, I wanted to study the effects of certain variables on the cooking time of rice.'' ``My wife and I were wondering if our oldest daughter had a favorite toy.'' ``For the home brewer, a small kitchen blender does a good job of grinding malt, provided the right levels of speed, batch size and time are used. This is the basis of the experimental design.'' ``During my career as a beer drinker, various questions have arisen.'' ``I do much of the maintenance and repair work around my home, and some of the repairs require the use of epoxy glue. I was curious about some of the factors affecting its performance.'' ``My wife and I are interested in indoor plants, and often we like to give them as gifts. We usually select a cutting from one of our fifty or so plants, put it in a glass of water until it develops roots, and then pot it. We have observed that sometimes the cutting roots quickly and sometimes it roots slowly, so we decided to experiment with several factors that we thought might be important in this process.'' ``I chose to find out how my shotguns were firing. I reload my own shells with powders that were recommended to me, one for short range shooting and one for long range shooting. I had my doubts if the recommendations were valid.''

What Did the Students Learn?

The conclusion reached in this last experiment was: ``As it looks now, I should use my Gun A with powder C for close range shooting, such as for grouse and woodcock. I should use gun B and powder D for longer range shooting as for ducks and geese.'' As is illustrated by this example and the first four discussed above, the students sometimes learned things that were directly useful to them. Some other examples: ``Spending $70 extra to buy tape deck 2 is not justified as the difference in sound is better with the other, or probably there is no difference. The synthesizer appears not to affect the quality of the sound.'' In operating my calculator I can anticipate increasing operation time by an additional 15 minutes and 23 seconds on the average by charging 60 minutes instead of 30 minutes.'' ``In conclusion, the Chinese dumplings turned out very pretty and very delicious, especially the ones with thin skins. I think this was a successful experiment.

Naturally, not all experiments were successful. ``A better way to have run the experiment would have been to...'' Various troubles arose. ``The reason that there is only one observation for the eighth row is that one of the cups was knocked over by a curious cat.'' ``One observation made during the experiment was that the child's posture may have affected the duration of the ride. Mark (13 pounds) leaned back, thus distributing his weight more evenly. On the other hand, Mike (22 pounds) preferred to sit forward, which may have made the restoring action of the spring more difficult.'' (The trouble here was that the variable the student wanted to study was weight, not posture.) Another student, who was studying factors that affected how fast snow melted on sidewalks, had some of his data destroyed because the sun came out brightly (and unexpectedly) one day near the end of his experiment and melted all the snow.

Because of such troubles these simple experiments have served as useful vehicles for discussing important practical points that arise in more serious scientific investigations. Excellent questions for this purpose have arisen from these studies. ``Do I really need to use a completely randomized experiment? It will take much longer to do that way?'' There have been good examples that illustrate the sequential nature of experimentation and show how carefully conceived experimental designs can help in solving problems.''...This must have been the main reason why the first experiment completely failed. I decided to try another factorial design. Synchronization of the flash unit and camera still bothered me. I decided to experiment with...'' some other factors.

As a result of these projects students seem to get a much better appreciation of the efficiency and beauty of experimental designs. For example, in this last experiment the student concluded: ``The factorial design proved to be efficient in solving the problem. I did get off on the wrong track initially, but the information learned concerning synchronization is quite valuable.'' Another student: ``It is interesting to see how a few experiments can give so much information.''

There is another point, and it is not the least important. Most of the students had fun with these projects. And I did, too. Just looking through Table 1 suggests why this is so, I think. One report ended simply: ``This experiment was really fun!'' Many students have reported that this was the best part of the course.

There is a tendency sometimes for experimenters to discount what they have learned, this being true not only for students in this class, but also for experimenters in general. That is, they learn more than they realize. Hindsight is the culprit. On pondering a certain conclusion, one is prone to say ``Oh yes, that makes sense. Yes, that's the way it should be. That's what I would have expected.'' While this reaction is often correct, one is sometimes just fooling oneself, that is, interrogation at the outset would have produced exactly the opposite opinion. So that students could more accurately gauge what they learned from their simple experiments, I tried the following and it seemed to work: after having decided on the experimental runs to perform, the student guessed what his or her major conclusions would be and wrote them down. Upon completion of the assignment, these guesses were checked against the actual results, which immediately provided a clear picture of what was learned (the surprises) and what was confirmed (the non-surprises).

I now tend to spend much more time introducing each new topic than I used to. Providing appropriate motivation is extremely important. For classes I have had the privilege of teaching-whether in universities or elsewhere-I have found that it has been better to use concrete examples followed by the general theory rather than the reverse. I now try to describe a particular problem in some detail, preferably a real one with which I am familiar, and then pose the question: What would YOU do? I find it helpful to resist the temptation to move on too quickly to the prepared lecture so that there is ample time for students to consider this question seriously, to discuss it, to ask questions of clarification, to express ideas they have, and ultimately (and this really the object of the exercise) to realize that a genuine problem exists and they do not know how to solve it. They are then eager to learn. And after we have finished with that particular topic they know they have learned something of value. (I realize as I write this that I have been strongly influenced by George Barnard, who masterfully conducted a seminar in this manner at Imperial College, London, in 1964-65, which I was fortunate to have attended.)

Current examples are well-received, especially controversies (for example, weather modification experiments). Some useful sources are court cases, advertisements, TV and radio commercials, and ``Consumer Reports''. An older controversy still of considerable interest from a pedagogical point of view is the AD-X2 battery additive case. Gosset's comments on the Lanarkshire Milk Experiment are still illuminating. Sometimes trying to get the data that support a particular TV commercial or the facts from both parties of a dispute has made an interesting side project to carry along through a semester.

Having each student exercise his or her own initiative in thinking up an experiment and carrying it through to completion has turned out successfully. Using games involving simulated data has also been useful. I have incorporated such projects, principally of the former type, into courses I have taught, and I urge others to consider doing the same. Why?

First of all, it's fun. The students have generally welcomed the opportunity to learn something about a particular question they have wondered about. I have been fascinated to see what they have chosen to study and what conclusions they have reached, so it has been fun for me, too. The students and I have certainly learned interesting things we did not know before. Why doesn't my bread rise? Why don't my flowers grow? Is this analytical procedure robust? Will carrying a crutch make it easier for me to get a ride hitchhiking? (Incidentally, it made it harder.)

Secondly, the students have gotten a lot out of such experiences. There is a definite deepening of understanding that comes from having been through a study from start to finish-deciding on a problem, the variables, the ranges of the variables, and how to measure the response(s), actually running the experiment and collecting the data, analyzing the results, learning what the practical consequences are, and finally writing a report. Being veterans, not of the war certainly but of a minor skirmish at least, the students seem more comfortable and confident with the entire subject of the design of experiments, especially as they share their experiences with one another.

Thirdly, I have found it particularly worthwhile to discuss with them in class some of the practical questions that naturally emerge from these studies. ``What can I do about missing data?'' ``These first three readings are questionable because I think I didn't have my technique perfected then-What should I do?'' ``A most unusual thing happened during this run, so should I analyze this result with all the others or leave it out?'' They are genuinely interested in such questions because they have actually encountered them, not just read about them in a textbook. Sometimes there is no simple answer, and lively and valuable discussions then occur. Such discussions, I hope, help them understand that, when they confront real problems later on which refuse to look like those in the textbooks no matter how they are viewed, there are alternatives to pretending they do and charging ahead regardless or forgetting about them in hopes they will go away or adopting a ``non-statistical'' approach-in a word, there are alternatives to panic.

Table 1. List of some studies done by students in an experimental design course.

• variables: seat height (26, 30 inches), generator (off,on), tire pressure (40, 55 psi) responses: time to complete fixed course on bicycle and pulse rate at finish
• variables: brand of popcorn (ordinary, gourmet), size of batch (1/3,2/3 cup), popcorn to oil ratio (low, high) responses: yield of popcorn
• variables: amount of yeast, amount of sugar, liquid (milk, water), rise temperature, rise time responses: quality of bread, especially the total rise
• variables: number of pills, amount of cough syrup, use of vaporizer responses: how well twins, who had colds, slept during the night
• variables: speed of film, light (normal, diffused), shutter speed responses: quality of slides made close up with flash attachment on camera
• variables: hours of illumination, water temperature, specific gravity of water responses: growth rate of algae in salt water aquarium
• variables: temperature, amount of sugar, food prior to drink (water, salted popcorn) responses: taste of Koolaid
• variables: direction in which radio is facing, antenna angle, antenna slant responses: strength of radio signal from particular AM station in Chicago
• variables: blending speed, amount of water, temperature of water, soaking time before blending responses: blending time for soy beans
• variables: charge time, digits fixed, number of calculations performed responses: operation time for pocket calculator
• variables: clothes dryer (A,B), temperature setting, load responses: time until dryer stops
• variables: pan (aluminum, iron), burner on stove, cover for pan (no, yes) responses: time to boil water
• variables: aspirin buffered? (no, yes) dose, water temperature responses: hours of relief from migraine headache
• variables: amount of milk powder added to milk, heating temperature, incubation temperature responses: taste comparison of homemade yogurt and commercial brand
• variables: pack on back (no, yes), footwear (tennis shoes, boots), run (7, 14 flights of steps) responses: time required to run up steps and heartbeat at top
• variables: width to height ratio of sheet of balsa wood, slant angle, dihedral angle, weight added, thickness of wood responses: length of flight of model airplane
• variables: level of coffee in cup, devices (nothing, spoon placed across top of cup facing up), speed of walking responses: how much coffee spilled while walking
• variables: type of stitch, yarn gauge, needle size responses: cost of knitting scarf, dollars per square foot
• variables: type of drink (beer, rum), number of drinks, rate of drinking, hours after last meal responses: time to get steel ball through a maze
• variables: size of order, time of day, sex of server responses: cost of order of french fries, in cents per ounce
• variables: brand of gasoline, driving speed, temperature responses: gas mileage for car
• variables: stamp (first class, air mail), zip code (used, not used), time of day when letter mailed responses: number of days required for letter to be delivered to another city
• variables: side of face (left, right), beard history (shaved once in two years0-sideburns, shaved over 600 times in two years-just below sideburns) responses: length of whiskers 3 days after shaving
• variables: eyes used (both, right), location of observer, distance responses: number of times (out of 15) that correct gender of passerby was determined by experimenter with poor eyesight wearing no glasses
• variables: distance to target, guns (A,B), powders(C,D) responses: number of shot that penetrated a one foot diameter circle on the target
• variables: oven temperature, length of heating, amount of water responses: height of cake
• variables: strength of developer, temperature, degree of agitation responses: density of photographic film
• variables: brand of rubber band, size, temperature responses: length of rubber band before it broke
• variables: viscosity of oil, type of pick-up shoes, number of teeth in gear responses: speed of H.O. scale slot racers
• variables: type of tire, brand of gas, driver (A,B) responses: time for car to cover one-quarter mile
• variables: temperature, stirring rate, amount of solvent responses: time to dissolve table salt
• variables: amounts of cooking wine, oyster sauce,sesame oil responses: taste of stewed chicken
• variables: type of surface, object (slide rule, ruler, silver dollar), pushed? (no,yes) responses: angle necessary to make object slide
• variables: ambient temperature, choke setting, number of charges responses: number of kicks necessary to start motorcycle
• variables: temperature, location in oven, biscuits covered while baking? (no,yes) responses: time to bake biscuits
• variables: temperature of water, amount of grease, amount of water conditioner responses: quantity of suds produced in kitchen blender
• variables: person putting daughter to bed (mother, father), bed time, place (home, grandparents) responses: toys child chose to sleep with
• variables: amount of light in room, type of music played, volume responses: correct answers on simple arithmetic test, time required to complete test, words remembered (from list of 15)
• variables: amounts of added Turkish, Latakia, and Perique tobaccos responses: bite, smoking characteristics, aroma, and taste of tobacco mixture
• variables: temperature, humidity, rock salt responses: time to melt ice
• variables: number of cards dealt at one time, position of picker relative to the dealer responses: points in games of sheepshead, a card game
• variables: marijuana (no, yes),tequila (no, yes),sauna (no, yes) responses: pleasure experienced in subsequent sexual intercourse
• variables: amounts of flour, eggs, milk responses: taste of pancakes, consensus of group of four living together
• variables: brand of suntan lotion, altitude, skier responses: time to get sun burned
• variables: amount of sleep the night before, substantial exercise during the day? (no, yes), eat right before going to bed? (no, yes) responses: soundness of sleep, average reading from five persons
• variables: brand of tape deck used for playing music, bass level, treble level, synthesizer? (no, yes) responses: clearness and quality of sound, and absence of noise
• variables: type of filter paper, beverage to be filtered, volume of beverage responses: time to filter
• variables: type of ski, temperature, type of wax responses: time to go down ski slope
• variables: ambient temperature for dough when rising, amount of vegetable oil, number of onions responses: four quality characteristics of pizza
• variables: amount of fertilizer, location of seeds (3 x 3 Latin square) responses: time for seeds to germinate
• variables: speed of kitchen blender, batch size of malt, blending time responses: quality of ground malt for brewing beer
• variables: soft drink (A,B), container (can, bottle), sugar free? (no, yes) responses: taste of drink from paper cup
• variables: child's weight (13, 22 pounds),spring tension (4, 8 cranks), swing orientation (level, tilted) responses: number of swings and duration of these swings obtained from an automatic infant swing
• variables: orientation of football, kick (ordinary, soccer style),steps taken before kick, shoe (soft, hard) responses: distance football was kicked
• variables: weight of bowling ball, spin, bowling lane (A, B) responses: bowling pins knocked down
• variables: distance from basket type of shot, location on floor responses: number of shots made (out of 10) with basketball
• variables: temperature, position of glass when pouring soft drink, amount of sugar added responses: amount of foam produced when pouring soft drink into glass
• variables: brand of epoxy glue, ratio of hardener to resin, thickness of application, smoothness of surface, curing time responses: strength of bond between two strips of aluminum
• variables: amount of plant hormone, water (direct from tap, stood out for 24 hours), window in which plant was put responses: root lengths of cuttings from purple passion vine after 21 days
• variables: amount of detergent (1/4, 1/2 cup), bleach (none, 1 cup), fabric softener (not used, used) responses: ability to remove oil and grape juice stains
• variables: skin thickness, water temperature, amount of salt responses: time to cook Chinese meat dumpling
• variables: appearance (with and without a crutch), location, time responses: time to get a ride hitchhiking and number of cars that passed before getting a ride
• variables: frequency of watering plants, use of plant food (no, yes), temperature of water responses: growth rate of house plants
• variables: plunger A up (slow, fast),plunger A down (slow, fast), plunger B up (slow, fast) plunger B down (slow, fast) responses: reproducibility of automatic diluter, optical density readings made with spectrophotometer
• variables: temperature of gas chromatograph column, tube type (U, J), voltage responses: size of unwanted droplet
• variables: temperature, gas pressure, welding speed responses: strength of polypropylene weld,manual operation
• variables: concentration of lysozyme, pH, ionic strength, temperature responses: rate of chemical reaction
• variables: anhydrous barium peroxide powder,sulfur,charcoal dust responses: length of time fuse powder burned and the evenness of burning
• variables: air velocity, air temperature, rice bed depth responses: time to dry wild rice
• variables: concentration of lactose crystal, crystal size, rate of agitation responses: spread ability of caramel candy
• variables: positions of coating chamber, distribution plate, and lower chamber responses: number of particles caught in a fluidized bed collector
• variables: proportional band, manual reset, regulator pressure responses: sensitivity of a pneumatic valve control system for a heat exchanger
• variables: chloride concentration, phase ratio, total amine concentration, amount of preservative added responses: degree of separation of zinc from copper accomplished by extraction
• variables: temperature, nitrate concentration, amount of preservative added responses: measured nitrate concentration in sewage, comparison of three different methods
• variables: solar radiation collector size, ratio of storage capacity to collector size, extent of short-term intermittency of radiation, average daily radiation on three successive days responses: efficiency of solar space-heating system, a computer simulation
• variables: pH, dissolved oxygen content of water, temperature responses: extent of corrosion of iron
• variables: amount of sulfuric acid, time of shaking milk-acid mixture, time of final tempering responses: measurement of butterfat content of milk
• variables: mode (batch, time-sharing), job size, system utilization (low, high) responses: time to complete job on computer
• variables: flow rate of carrier gas, polarity of stationary liquid phase, temperature responses: two different measures of efficiency of operation of gas chromatograph
• variables: pH of assay buffer, incubation time, concentration of binder responses: measured cortisol level in human blood plasma
• variables: aluminum, boron, cooling time responses: extent of rock candy fracture of cast steel
• variables: magnification, read out system (micrometer, electronic), stage light responses: measurement of angle with photogrammetric instrument
• variables: riser height, mold hardness, carbon equivalent responses: changes in height, width, and length dimensions of cast metal
• variables: amperage, contact tube height, travel speed, edge preparation responses: quality of weld made by submerged arc welding process
• variables: time, amount of magnesium oxide, amount of alloy responses: recovery of material by steam distillation
• variables: pH, depth, time responses: final moisture content of alfalfa protein
• variables: deodorant, concentration of chemical, incubation time responses: odor produced by material isolated from decaying manure, after treatment
• variables: temperature variation, concentration of cupric sulfate concentration of sulfuric acid responses: limiting currents on totaling disk electrode
• variables: air flow, diameter of bead, heat shield (no, yes) responses: measured temperature of a heated plate
• variables: voltage, warm-up procedure, bulb age responses: sensitivity of micro densitometer
• variables: pressure, amount of ferric chloride added, amount of lime added responses: efficiency of vacuum filtration of sludge
• variables: longitudinal feed rate, transverse feed rate, depth of cut responses: longitudinal and thrust forces for surface grinding operation
• variables: time between preparation of sample and refluxing, reflux time, time between end of reflux and start of titrating responses: chemical oxygen demand of samples with same amount of waste (acetanilide)
• variables: speed of rotation, thrust load, method of lubrication responses: torque of taper roller bearings
• variables: type of activated carbon, amount of carbon, pH responses: adsorption characteristics of activated carbon used with municipal waste water
• variables: amounts of nickel, manganese, carbon responses: impact strength of steel alloy
• variables: form (broth, gravy), added broth (no, yes), added fat (no, yes), type of meat (lamb, beef) responses: percentage of panelists correctly identifying which samples were lamb
• variables: well (A, B), depth of probe, method of analysis (peak height, planimeter) responses: methane concentration in completed sanitary landfill
• variables: paste (A, B), preparation of skin (no, yes), site (sternum, forearm) responses: electrocardiogram reading
• variables: lime dosage, time of flocculation, mixing speed responses: removal of turbidity and hardness from water
• variables: temperature difference between surface and bottom waters, thickness of surface layer, jet distance to thermocline, velocity of jet, temperature difference between jet and bottom waters responses: mixing time for an initially thermally stratified tank of water

Articles by Bill

Thoughts about bill's work and life, articles by george box.

Critical Thinking in Science

Part 1: Introduction to Experimental Design

• Part 2: The Story of Pi
• Part 3: Experimenting with pH
• Part 4: Water Quality
• Part 5: Change Over Time
• Part 6: Cells
• Part 7: Microbiology and Infectious Disease
• About the Author

Introduction:

Students will learn and implement experimental design vocabulary while practicing their critical thinking skills in an inquiry based experiment. This lesson is written using the 5E Learning Model.

Learning Outcomes:

• Students will define and apply the experimental design vocabulary.
• Students will use the experimental design graphic organizer to plan an investigation.
• Students will design and complete their own scientific experiment.

Curriculum Alignment:


1.01 Identify and create questions and hypotheses that can be answered through scientific investigations.

1.02 Develop appropriate experimental procedures for:

• Given questions.
• Student generated questions.

1.04 Analyze variables in scientific investigations:

• Identify dependent and independent.
• Use of a control.
• Manipulate.
• Describe relationships between.
• Define operationally.

1.05 Analyze evidence to:

• Explain observations.
• Make inferences and predictions.
• Develop the relationship between evidence and explanation.

1.06 Use mathematics to gather, organize, and present quantitative data resulting from scientific investigations:

• Measurement.
• Analysis of data.
• Prediction models.

1.08 Use oral and written language to:

• Communicate findings.
• Defend conclusions of scientific investigations.
• Describe strengths and weaknesses of claims, arguments, and/or data

Classroom Time Required:

Approximately 6 class periods (~50 minutes each) are needed, however, some things can be assigned as homework to decrease the time spent in class.

Materials Needed:

• Overhead transparency of Experimental Design Graphic Organizer
• Student copies of: Experimental Design Graphic Organizer, Vocabulary Graphic Organizer, Explore worksheet, Explain worksheet
• Supplies for experiment: Dixie drinking cups, Pepsi and Coke (~1.5 to 2 ounces per student) Possibly ice to keep soda cold
• Copies of worksheet, Overhead, Small drinking cups (2 per student), Pepsi and Coke
• Dictionaries or definition sheets for the vocabulary words

Technology Resources:

• Overhead Projector

Pre-Activities/ Activities:

• What are the “rules” for designing an experiment?
• Teacher and class will discuss the following questions:
• Is there a specific way to design an experiment? (Try to get them to mention the scientific method and discuss any “holes” in this.
• Are their rules scientists follow when designing an experiment?
• Are all experiments designed the same?
• What kinds of experiments have you done on your own? (Good things to discuss are cooking, testing sports techniques, trying to fix things, etc….. Try to relate experimentation to their everyday life.)
• Review an experiment and answer questions.
• Students will read a description of an experiment and answer questions about the design of the experiment without using the vocabulary. (See Worksheet 1)
• Vocabulary introduction and application
• Students will define the experimental design vocabulary using the graphic organizer (See Worksheet 2).
• Independent variable, Dependent variable, Control, Constant, Hypothesis, Qualitative observation, Quantitative observation, Inference (Definitions available)
• Students will review the worksheet from the explore section and match the vocabulary to the pieces of the experiment. Review answers with the class.
• Students will read a second experiment description and identify the pieces of the experiment using their vocabulary definitions (See Worksheet 3).
• Introduce Experimental Design Graphic Organizer (EDGO) and complete class designed experiment.
• The teacher should review the EXAMPLE PEPSI VS COKE EDGO for any ideas or questions
• Use overhead projector to review the blank EDGO and complete as a class (See Worksheet 4)
• Tell the class that you are going to do the Pepsi Coke Challenge. The question they need to answer is: Can girls taste the difference between Pepsi and Coke better than the boys?
• As a class, plan the Pepsi verses Coke experiment. This is a good time to discuss double blind studies and why it is important to make this a double blind study. Students can look at the results within their own class as well as the whole team.
• This is a good chance to also test multiple variables. You do not need to let students know this, but if the data chart also records things like age, frequency of drinking soda (daily, weekly, monthly, rarely), ability to roll tongue, or anything else they think might be interested in, the results can be analyzed for each variable.
• I removed labels from the bottles and labeled them A and B. I used a different labeling system for under the cups so the students did not see a pattern (numbered cups were Pepsi, lettered cups were Coke).
• I recorded the data and organized the tasting while students completed other work in their seats. Two students at a time tasted the soda and I recorded data. You could also have a volunteer who is not participating help with this.
• Check for students who do not want to drink soda as well as any dietary needs such as diet soda.
• Do not verify guesses until all of the classes have completed the experiment.
• How can you accurately remember the pieces of an experiment?
• Write a poem about four of the vocabulary words.
• Write a song about four of the vocabulary words.
• Create a memorization tool for four of the vocabulary words.
• Make a poster about four of the vocabulary words.

Teachers should evaluate these choices to ensure they show an understanding of the vocabulary.

Assessment:

See Evaluate piece of Activities Section.

Modifications:

• A different experiment can be designed in the Elaborate section.
• EDGO can be edited for any motor skill deficiencies by making it larger, or making it available to be typed on.
• All basic modifications can be used for these activities.

Alternative Assessments:

• Make necessary adjustments for different experiments.

Critical Vocabulary

• Independent Variable- the part of the experiment that is controlled or changed by the experimenter
• Dependent Variable- the part of the experiment that is observed or measured to gather data; changes because of the independent variable
• Control- standard of comparison in the experiment; level of the independent variable that is left in the natural state, unchanged
• Constant- part of the experiment that is kept the same to avoid affecting the independent variable
• Hypothesis- educated guess or prediction about the experimental results
• Qualitative observation- word observations such as color or texture
• Quantitative observation- number observations or measurements
• Inference- attempt to explain the observations

This is the first lesson in the Critical Thinking in Science Unit. The other lessons continue using the vocabulary and Experimental Design Graphic Organizer while teaching the 8th grade content. Students are designing their own experiments to improve their ability to approach problems and questions scientifically. By developing their ability to reason through problems they are becoming critical thinkers.

Supplemental Files: 

Address: Campus Box 7006. Raleigh, NC 27695 Telephone: 919.515.5118 Fax: 919.515.5831 E-Mail: [email protected]

5 Ways to Make Experimental Design A More Approachable Topic

Introduction: Experimental Design

The research world is an entirely different zone to be in. It doesn’t work as per your beliefs. It doesn’t care about your personal whims and fancies. It has some basic rules that each young or seasoned scientist must follow. These are called ‘ research methodologies and ethics ’.

When students newly enter scientific research labs for graduate courses and further research activities, they are taken aback by the meticulously laid out formats following which one designs their experimental plans. While we all have our ideas and opinions which are celebrated in science, we are taught how to test the truth behind those ideas and opinions. And this is where the journey of a young scientist kicks off!

The experimental design allows one to incorporate as many variables as one wishes to test . This gives researchers the freedom to delve deeper into the topics and provide novel findings to the world through the medium of their research work. Several new concepts are introduced to students under the umbrella of experimental design. When young learners understand the importance of all of these concepts and apply as many concepts as they can in their work, it makes their findings stand the test of time, rigorous peer reviews and garner acceptance from both academia and industries.

With a lot in the box regarding experimental design for young learners, this topic can sometimes be a little too much for them. Educators introducing students to the world of research are sometimes in a dilemma of what and what not to teach. Teaching less won’t shape the students properly and teaching too much would be equivalent to spoon-feeding. Striking for a perfect balance can naturally sometimes be hard for educators when delivering the idea of experimental designs.

We, at Labster, understand this problem faced by students and teachers alike. In an ode to making the future generation of scientists well-equipped and confident in the scientific designing of their experiments, we have narrowed down the major problems faced by students to three. We try to highlight them so that educators can pay due attention and cater to their respective needs.

We also list practical solutions that educators can put to use in their next classes. Having scientific ideas is very different from executing those ideas. The latter deserves immediate attention and we aim to maximally resolve this issue. By the end of this article, we’ll convince you why a virtual lab simulation will prove wondrous not only for your students but also for you as an educator to deliver concepts more efficiently.

3 reasons why Experimental Design can be tricky to teach or learn

There are 3 major reasons why students are overwhelmed by the topic of Experimental Design. Acknowledging these issues is the first step toward making the topic more approachable. 

1. Too many rules

When students are introduced to the experimental design, usually it comes to them as a “rulebook” that they need to abide by. For young learners to whom the rationales behind these small intricacies haven’t been explained, it can feel like a very ‘restrictive’ way of doing science. Students from high school and university levels enter research labs dreaming to innovate something but the idea of experimental design without knowing its importance tends to kill all their enthusiasm. They are taught to play by the rules. And who loves rules?

2. No time for proper mentorship

Many students are introduced to the topic of experimental design without educating them about its importance. Though they are made to look at the brighter side, the lack of reason and rational approach makes it look worthless. This clear lack of mentorship bereft them of the potential that they have to do science and innovate.  Not knowing how to practically approach a scientific problem in the lab, design your experiments in a way that can take care of the major issues, or make sense of the collected data can be very uncomfortable situations to be in. Many students are particularly unmotivated to do good science because of this reason. Lack of mentorship is reflected in the lack of scientific aptitude, temperament and incompetence in building meaningful hypotheses in the students. 

3. A lot of new ideas 

Students new to research and independent lab handling are sometimes unaware of the concepts of ‘ null and alternative hypothesis , ‘ hypothesis testing-accepting or rejecting the null hypothesis , ‘ experimental or model organisms and factors to make an informed choice for the same ’, ‘ importance of controls and its types-negative, positive controls’ , ‘ importance of replicating experiments- biological replicates vs technical replicates’ and many more similar concepts. Ignorance of these concepts can harm the way science is practiced. It can make scientific experiments unreliable and irreproducible. 

5 ways to make Experimental Design a more approachable topic to understand

To address the issues encountered while teaching Experimental Design, educators can engage the under-listed solutions in their next classes. These can clarify many instrumental aspects of the topic and help them build strong foundations over which the science of the future will rest. Not only can they make teaching easier for educators like you but will also make lessons clearer and easier to assimilate for your students.

1. Explain its importance

Explaining the importance of experimental design can be a very good way to begin the discussion in your next class. Experimental designs have “ several components ” around which different rules have been set. You can take an advent and explain the importance of all those components. This little exercise will help your students appreciate and recognize the importance of the rulebook.

Variable Factors : This is one of the most important components of experimental design. There are several “ dependent”, “independent” and “controlled” variables that are important to study the effect of changes on our model system. Knowing the difference between these can help your students in the appropriate selection and tuning of factors for an experiment. 

Hypothesis : This is another important component of experimental design. Usually, several ideas pop up in our heads when we encounter a scientific problem or a gap in some research work. To work out that problem, we first need to formulate a hypothesis. There are usually 2 hypotheses that one needs to frame; “ null hypothesis ” and “ alternate hypothesis ”. Educate your students on how one accepts and rejects (fails to accept) the null hypothesis. 

Controls : Controls are another important and non-negotiable part of a good experimental design. When you’re studying the effect of a variable on a system, you must have a “ control ” in place. Controls help us navigate and reaffirm if the changes/no changes are really due to the fine-tuning of variables or not. Educate your students about the “treatment versus control comparisons” to make a final call. 

Setting controls is very important to conduct a scientifically sound experiment. As mentioned by John S Torday and František Baluška in their paper ‘ Why control an experiment ?’ published in 2019, the importance of controls is undeniable.

“… once we began our formal training as scientists, the greatest challenge beyond formulating a testable or refutable hypothesis was designing appropriate controls for an experiment.”

Figure:An interactive snippet from the Experimental Design simulation from Labster. It is available for High School, University/College and Professional courses .

2. Take it slow and promote healthy discussions

It’s important that your students feel free to question and seek answers to the ideas that are being delivered to them here. So, we recommend educators promote a culture of open discussions where students aren’t looked down upon for any type of queries or doubts. 

As a scientist, one can have a very simple problem or a very complex one; it depends solely on how one approaches it. So, giving time to your students to slowly assimilate the notions being taught in the class would be highly beneficial in the long run. 

Explain the ideas in an easily understandable language. We know that academia and particularly scientific terminologies are hard to simplify, but that’s the job of educators. We leave you with an example that you can explain in your next class.

Example-1: “ Replicates, types and importance”

In scientific experiments, the value of replicates can never be overemphasized. A replicate is a repetition of our experiment with the same setting, same variables, and same conditions and treatment. They are important to ensure that the new changes that are observed after an experiment has resulted due to the variable (modified factor), or is it just due to some other random condition? Several replicates are commonly used, biological and technical replicates being the most important ones. Usually, the number of replicates is restricted to 3 or 5. 

Example-2: “ Hypothesis testing ”

Scientific hypotheses are ideas and notions believing in which scientists begin their work. These are tentatively considered the “ best available explanations” for a particular research question. Explain to your students that until their proposed scientific ideas have withstood the test of time and in-depth inquiry, they cannot be considered to be the final word. This fosters in them a scientific temperament and permits the conduct of the real scientific study. Demonstrate to your students how a hypothesis's formulation and objective evaluation aid in the development of "TRUE experimental conclusions”.  

3. Make it more interactive with classroom activities 

Engaging your students in small classroom activities can be another way to help them understand the idea of experimental designs. Educators can help their students navigate through the problem scientifically. Here’s a stepwise example.

Teach them to “ ask a question ”.

Guide them in developing a testable hypothesis.

Help them choose an apt experimental model.

Educate them about the proper selection of variables and controls.

Navigate them through the real performance of an experiment.

Make them capable of analyzing their results.

Teach them the importance of replicating their experiments, so “repeat it all over again”!'

Figure: Flowchart showing how experimental designs should be built. Image Source

4. Explain the importance of common terms

Several terms are common to all experimental designs, whether it be biochemistry, botany, zoology, or medicine. We provide a few examples that you can explain and discuss in your next class. 

Model organisms or Experimental models

Reliability versus Reproducibility versus Repeatability 

Positive versus Negative controls

Research Methodologies and Ethics 

5. Use virtual lab simulations

Since students often find it hard to assimilate so many novel ideas at once, the topic of experimental design and its components can be quite taxing for them. To make the class interesting and free-flowing for your students, we encourage modern-day educators to use the Experimental Design simulation from Labster.

Using this active way of teaching students, you can overcome passive engagement and convey deeper ideas to your students by offering them visually dynamic video graphics options. Young scientists must establish their scientific aptitude and research temperament at a young age.  This could pave the way for science that transforms the globe in the upcoming decade. Our gamification features and interactive simulations are revolutionizing the way science is taught in classes. By using this way of active and immersive teaching, our virtual learning platform takes an advent in the field of Science to make the upcoming scientists thorough with the “basics of their respective subjects”. 

You can learn more about the Experimental Design simulation from Labster here or get in touch to find out how you can start using virtual labs with your students.

Labster helps universities and high schools enhance student success in STEM.

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Tree-based machine learning and nelder–mead optimization for optimized cr(vi) removal with indian gooseberry seed powder.

1. Introduction

1.1. motivation, 1.2. the literature, 1.3. research gaps.

• The literature indicates that the traditional statistical approach of BBDs in the RSM for optimizing Cr(VI) removal from wastewater struggle to capture nonlinear relationships between the process parameters and removal efficiency.
• In addition, the focus of conventional experimental approaches for Cr(VI) removal using bio-absorption materials, which are lacking in terms of identifying optimal combinations of process parameters, may lead to increased experimentation time and cost.
• Furthermore, previous research by Krishna et al. [ 14 ], which explored the use of Indian gooseberry seed powder as an adsorbent for Cr(VI) removal, had a gap in the application of ML models for analyzing and optimizing this process. Similarly, the existing literature also does not fully capture the intricate interactions between various process parameters (such as the initial Cr(VI) concentration, pH level, and adsorbent dosage) and their impact on removal efficiency.

1.4. Novelty

1.5. major contributions.

• Better prediction of efficiency in removing Cr(VI): ML models outperform conservative BBD approaches in realizing sophisticated nonlinear connections. Consequently, this permits more exact estimations of Cr(VI) removal efficiency using different process parameters.
• Improved maximization through optimization: The proposed approach employs ML-based Nelder–Mead optimization for maximizing Cr(VI) removal, and it reduces the experimentation time and treatment cost and allows efficient processing of larger wastewater volumes.
• Integration of ML models with optimization: The combination of ML models with optimization is a novel approach which has not been previously reported in the literature. Moreover, it offers a new direction for exploring this bio-absorption material.

1.6. Organization

2. materials and methods, 2.1. summary of experimental investigations, 2.2. dataset, 3. proposed model, 3.1. curve fitting, 3.2. synthesized dataset.

3.3. Scaling and Splitting

3.4. model building and training, 3.5. model testing.

• Mean Absolute Error (MAE): The absolute average of the difference between the ACrVI and PCrVI of all testing data instances divided by the total number of instances, as shown in Equations ( 3 ) and ( 4 ), is known as the MAE:
• Mean Squared Error (MSE): The summation of the squares of the differences of all the actual and predicted chromium VI removal percentages, as shown in Equation ( 1 ), divided by number of testing samples, as reported in Equation ( 5 ), is known as the MSE.
• Root Mean Squared Error (RMSE): Also called the root mean square deviation (RMSD) or root mean squared error on prediction (RMSE), the square root of summation of the squared residuals divided by the total number of instances, as reported in Equation ( 6 ), is known as the RMSE:
• R 2 –Score : Also called the coefficient of determination, this specifies the variance or score of a model based on given test data and indicates how much of the variance in the dependent features is explained by an independent feature. Equations ( 7 ) and ( 8 ) show the mathematical formulas for the R 2 score’s calculation, where a continuous value between 0 and 1 indicates model score and a model score near one indicates that the model performance is good with minimal error:
• Relative Root Mean Squared Error (RRMSE): The RRMSE is calculated as stated in Equation ( 9 ). The model performance is expressed as a percentage. A model with a value < 10% is said to be excellent, while it is good if it is between 10% and 20%, fair if it is between 20% and 30%, and poor if it is above 30%:
• Chromium (VI) removal percentage: In the traditional approach, chromium (VI) removal efficiency [ 14 , 31 ] is measured as shown in Equation ( 10 ). However, in this work, the synthesized dataset with 2000 instances was built using original experimental data (shown in Table 1 ) by varying the initial chromium (VI) concentration (20–100 mg/L), pH level (1–5), and Indian gooseberry powder dosage (2–10 g/L). The synthesized data samples were given as testing data to all trained ML models to determine the optimal values which removed the highest percentage of chromium (VI) from synthetic wastewater for the three independent features (“initial concentration of Cr(VI)”, “pH”, and “Adsorbent dosage”). During the prediction procedure, comparison analysis of the three ML models through six evolution metrics—the MAE, MSE, RMSE, R 2 –Score, and RRMSE—is presented in Table 5 and the optimal values for the maximum percentage of chromium (VI) removal are presented in Table 6 .

3.6. Nelder–Mead Optimization

4. results and discussions, validation of the optimization results, 5. conclusions, author contributions, data availability statement, acknowledgments, conflicts of interest.

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Kalabarige, L.R.; Krishna, D.; Potnuru, U.K.; Mishra, M.; Alharthi, S.S.; Koutavarapu, R. Tree-Based Machine Learning and Nelder–Mead Optimization for Optimized Cr(VI) Removal with Indian Gooseberry Seed Powder. Water 2024 , 16 , 2175. https://doi.org/10.3390/w16152175

Kalabarige LR, Krishna D, Potnuru UK, Mishra M, Alharthi SS, Koutavarapu R. Tree-Based Machine Learning and Nelder–Mead Optimization for Optimized Cr(VI) Removal with Indian Gooseberry Seed Powder. Water . 2024; 16(15):2175. https://doi.org/10.3390/w16152175

Kalabarige, Lakshmana Rao, D. Krishna, Upendra Kumar Potnuru, Manohar Mishra, Salman S. Alharthi, and Ravindranadh Koutavarapu. 2024. "Tree-Based Machine Learning and Nelder–Mead Optimization for Optimized Cr(VI) Removal with Indian Gooseberry Seed Powder" Water 16, no. 15: 2175. https://doi.org/10.3390/w16152175

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