a simple example of hypothesis

How to Write a Hypothesis in 6 Steps, With Examples

A hypothesis is a statement that explains the predictions and reasoning of your research—an “educated guess” about how your scientific experiments will end. As a fundamental part of the scientific method, a good hypothesis is carefully written, but even the simplest ones can be difficult to put into words. 

Want to know how to write a hypothesis for your academic paper ? Below we explain the different types of hypotheses, what a good hypothesis requires, the steps to write your own, and plenty of examples.

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What is a hypothesis? 

One of our 10 essential words for university success , a hypothesis is one of the earliest stages of the scientific method. It’s essentially an educated guess—based on observations—of what the results of your experiment or research will be. 

Some hypothesis examples include:

• If I water plants daily they will grow faster.
• Adults can more accurately guess the temperature than children can. 
• Butterflies prefer white flowers to orange ones.

If you’ve noticed that watering your plants every day makes them grow faster, your hypothesis might be “plants grow better with regular watering.” From there, you can begin experiments to test your hypothesis; in this example, you might set aside two plants, water one but not the other, and then record the results to see the differences. 

The language of hypotheses always discusses variables , or the elements that you’re testing. Variables can be objects, events, concepts, etc.—whatever is observable. 

There are two types of variables: independent and dependent. Independent variables are the ones that you change for your experiment, whereas dependent variables are the ones that you can only observe. In the above example, our independent variable is how often we water the plants and the dependent variable is how well they grow. 

Hypotheses determine the direction and organization of your subsequent research methods, and that makes them a big part of writing a research paper . Ultimately the reader wants to know whether your hypothesis was proven true or false, so it must be written clearly in the introduction and/or abstract of your paper. 

7 examples of hypotheses

Depending on the nature of your research and what you expect to find, your hypothesis will fall into one or more of the seven main categories. Keep in mind that these categories are not exclusive, so the same hypothesis might qualify as several different types. 

1 Simple hypothesis

A simple hypothesis suggests only the relationship between two variables: one independent and one dependent. 

• If you stay up late, then you feel tired the next day. 
• Turning off your phone makes it charge faster. 

2 Complex hypothesis

A complex hypothesis suggests the relationship between more than two variables, for example, two independents and one dependent, or vice versa. 

• People who both (1) eat a lot of fatty foods and (2) have a family history of health problems are more likely to develop heart diseases. 
• Older people who live in rural areas are happier than younger people who live in rural areas. 

3 Null hypothesis

A null hypothesis, abbreviated as H 0 , suggests that there is no relationship between variables. 

• There is no difference in plant growth when using either bottled water or tap water. 
• Professional psychics do not win the lottery more than other people. 

4 Alternative hypothesis

An alternative hypothesis, abbreviated as H 1 or H A , is used in conjunction with a null hypothesis. It states the opposite of the null hypothesis, so that one and only one must be true. 

• Plants grow better with bottled water than tap water. 
• Professional psychics win the lottery more than other people. 

5 Logical hypothesis

A logical hypothesis suggests a relationship between variables without actual evidence. Claims are instead based on reasoning or deduction, but lack actual data.  

• An alien raised on Venus would have trouble breathing in Earth’s atmosphere. 
• Dinosaurs with sharp, pointed teeth were probably carnivores. 

6 Empirical hypothesis

An empirical hypothesis, also known as a “working hypothesis,” is one that is currently being tested. Unlike logical hypotheses, empirical hypotheses rely on concrete data. 

• Customers at restaurants will tip the same even if the wait staff’s base salary is raised. 
• Washing your hands every hour can reduce the frequency of illness. 

7 Statistical hypothesis

A statistical hypothesis is when you test only a sample of a population and then apply statistical evidence to the results to draw a conclusion about the entire population. Instead of testing everything , you test only a portion and generalize the rest based on preexisting data. 

• In humans, the birth-gender ratio of males to females is 1.05 to 1.00.  
• Approximately 2% of the world population has natural red hair. 

What makes a good hypothesis?

No matter what you’re testing, a good hypothesis is written according to the same guidelines. In particular, keep these five characteristics in mind: 

Cause and effect

Hypotheses always include a cause-and-effect relationship where one variable causes another to change (or not change if you’re using a null hypothesis). This can best be reflected as an if-then statement: If one variable occurs, then another variable changes. 

Testable prediction

Most hypotheses are designed to be tested (with the exception of logical hypotheses). Before committing to a hypothesis, make sure you’re actually able to conduct experiments on it. Choose a testable hypothesis with an independent variable that you have absolute control over. 

Independent and dependent variables

Define your variables in your hypothesis so your readers understand the big picture. You don’t have to specifically say which ones are independent and dependent variables, but you definitely want to mention them all. 

Candid language

Writing can easily get convoluted, so make sure your hypothesis remains as simple and clear as possible. Readers use your hypothesis as a contextual pillar to unify your entire paper, so there should be no confusion or ambiguity. If you’re unsure about your phrasing, try reading your hypothesis to a friend to see if they understand. 

Adherence to ethics

It’s not always about what you can test, but what you should test. Avoid hypotheses that require questionable or taboo experiments to keep ethics (and therefore, credibility) intact.

How to write a hypothesis in 6 steps

1 ask a question.

Curiosity has inspired some of history’s greatest scientific achievements, so a good place to start is to ask yourself questions about the world around you. Why are things the way they are? What causes the factors you see around you? If you can, choose a research topic that you’re interested in so your curiosity comes naturally. 

2 Conduct preliminary research

Next, collect some background information on your topic. How much background information you need depends on what you’re attempting. It could require reading several books, or it could be as simple as performing a web search for a quick answer. You don’t necessarily have to prove or disprove your hypothesis at this stage; rather, collect only what you need to prove or disprove it yourself. 

3 Define your variables

Once you have an idea of what your hypothesis will be, select which variables are independent and which are dependent. Remember that independent variables can only be factors that you have absolute control over, so consider the limits of your experiment before finalizing your hypothesis. 

4 Phrase it as an if-then statement

When writing a hypothesis, it helps to phrase it using an if-then format, such as, “ If I water a plant every day, then it will grow better.” This format can get tricky when dealing with multiple variables, but in general, it’s a reliable method for expressing the cause-and-effect relationship you’re testing. 

5  Collect data to support your hypothesis

A hypothesis is merely a means to an end. The priority of any scientific research is the conclusion. Once you have your hypothesis laid out and your variables chosen, you can then begin your experiments. Ideally, you’ll collect data to support your hypothesis, but don’t worry if your research ends up proving it wrong—that’s all part of the scientific method. 

6 Write with confidence

Last, you’ll want to record your findings in a research paper for others to see. This requires a bit of writing know-how, quite a different skill set than conducting experiments. 

That’s where Grammarly can be a major help; our writing suggestions point out not only grammar and spelling mistakes , but also new word choices and better phrasing. While you write, Grammarly automatically recommends optimal language and highlights areas where readers might get confused, ensuring that your hypothesis—and your final paper—are clear and polished.

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when  conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a  correlational study  can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Hypothesis Examples

A hypothesis is a prediction of the outcome of a test. It forms the basis for designing an experiment in the scientific method . A good hypothesis is testable, meaning it makes a prediction you can check with observation or experimentation. Here are different hypothesis examples.

Null Hypothesis Examples

The null hypothesis (H 0 ) is also known as the zero-difference or no-difference hypothesis. It predicts that changing one variable ( independent variable ) will have no effect on the variable being measured ( dependent variable ). Here are null hypothesis examples:

• Plant growth is unaffected by temperature.
• If you increase temperature, then solubility of salt will increase.
• Incidence of skin cancer is unrelated to ultraviolet light exposure.
• All brands of light bulb last equally long.
• Cats have no preference for the color of cat food.
• All daisies have the same number of petals.

Sometimes the null hypothesis shows there is a suspected correlation between two variables. For example, if you think plant growth is affected by temperature, you state the null hypothesis: “Plant growth is not affected by temperature.” Why do you do this, rather than say “If you change temperature, plant growth will be affected”? The answer is because it’s easier applying a statistical test that shows, with a high level of confidence, a null hypothesis is correct or incorrect.

Research Hypothesis Examples

A research hypothesis (H 1 ) is a type of hypothesis used to design an experiment. This type of hypothesis is often written as an if-then statement because it’s easy identifying the independent and dependent variables and seeing how one affects the other. If-then statements explore cause and effect. In other cases, the hypothesis shows a correlation between two variables. Here are some research hypothesis examples:

• If you leave the lights on, then it takes longer for people to fall asleep.
• If you refrigerate apples, they last longer before going bad.
• If you keep the curtains closed, then you need less electricity to heat or cool the house (the electric bill is lower).
• If you leave a bucket of water uncovered, then it evaporates more quickly.
• Goldfish lose their color if they are not exposed to light.
• Workers who take vacations are more productive than those who never take time off.

Is It Okay to Disprove a Hypothesis?

Yes! You may even choose to write your hypothesis in such a way that it can be disproved because it’s easier to prove a statement is wrong than to prove it is right. In other cases, if your prediction is incorrect, that doesn’t mean the science is bad. Revising a hypothesis is common. It demonstrates you learned something you did not know before you conducted the experiment.

Test yourself with a Scientific Method Quiz .

• Mellenbergh, G.J. (2008). Chapter 8: Research designs: Testing of research hypotheses. In H.J. Adèr & G.J. Mellenbergh (eds.), Advising on Research Methods: A Consultant’s Companion . Huizen, The Netherlands: Johannes van Kessel Publishing.
• Popper, Karl R. (1959). The Logic of Scientific Discovery . Hutchinson & Co. ISBN 3-1614-8410-X.
• Schick, Theodore; Vaughn, Lewis (2002). How to think about weird things: critical thinking for a New Age . Boston: McGraw-Hill Higher Education. ISBN 0-7674-2048-9.
• Tobi, Hilde; Kampen, Jarl K. (2018). “Research design: the methodology for interdisciplinary research framework”. Quality & Quantity . 52 (3): 1209–1225. doi: 10.1007/s11135-017-0513-8

Related Posts

Educational resources and simple solutions for your research journey

What is a Research Hypothesis: How to Write it, Types, and Examples

Any research begins with a research question and a research hypothesis . A research question alone may not suffice to design the experiment(s) needed to answer it. A hypothesis is central to the scientific method. But what is a hypothesis ? A hypothesis is a testable statement that proposes a possible explanation to a phenomenon, and it may include a prediction. Next, you may ask what is a research hypothesis ? Simply put, a research hypothesis is a prediction or educated guess about the relationship between the variables that you want to investigate.  

It is important to be thorough when developing your research hypothesis. Shortcomings in the framing of a hypothesis can affect the study design and the results. A better understanding of the research hypothesis definition and characteristics of a good hypothesis will make it easier for you to develop your own hypothesis for your research. Let’s dive in to know more about the types of research hypothesis , how to write a research hypothesis , and some research hypothesis examples .  

Table of Contents

What is a hypothesis ?  

A hypothesis is based on the existing body of knowledge in a study area. Framed before the data are collected, a hypothesis states the tentative relationship between independent and dependent variables, along with a prediction of the outcome.  

What is a research hypothesis ?  

Young researchers starting out their journey are usually brimming with questions like “ What is a hypothesis ?” “ What is a research hypothesis ?” “How can I write a good research hypothesis ?”   

A research hypothesis is a statement that proposes a possible explanation for an observable phenomenon or pattern. It guides the direction of a study and predicts the outcome of the investigation. A research hypothesis is testable, i.e., it can be supported or disproven through experimentation or observation.     

Characteristics of a good hypothesis  

Here are the characteristics of a good hypothesis :  

• Clearly formulated and free of language errors and ambiguity  
• Concise and not unnecessarily verbose  
• Has clearly defined variables  
• Testable and stated in a way that allows for it to be disproven  
• Can be tested using a research design that is feasible, ethical, and practical   
• Specific and relevant to the research problem  
• Rooted in a thorough literature search  
• Can generate new knowledge or understanding.  

How to create an effective research hypothesis  

A study begins with the formulation of a research question. A researcher then performs background research. This background information forms the basis for building a good research hypothesis . The researcher then performs experiments, collects, and analyzes the data, interprets the findings, and ultimately, determines if the findings support or negate the original hypothesis.  

Let’s look at each step for creating an effective, testable, and good research hypothesis :  

• Identify a research problem or question: Start by identifying a specific research problem.   
• Review the literature: Conduct an in-depth review of the existing literature related to the research problem to grasp the current knowledge and gaps in the field.   
• Formulate a clear and testable hypothesis : Based on the research question, use existing knowledge to form a clear and testable hypothesis . The hypothesis should state a predicted relationship between two or more variables that can be measured and manipulated. Improve the original draft till it is clear and meaningful.  
• State the null hypothesis: The null hypothesis is a statement that there is no relationship between the variables you are studying.   
• Define the population and sample: Clearly define the population you are studying and the sample you will be using for your research.  
• Select appropriate methods for testing the hypothesis: Select appropriate research methods, such as experiments, surveys, or observational studies, which will allow you to test your research hypothesis .  

Remember that creating a research hypothesis is an iterative process, i.e., you might have to revise it based on the data you collect. You may need to test and reject several hypotheses before answering the research problem.  

How to write a research hypothesis  

When you start writing a research hypothesis , you use an “if–then” statement format, which states the predicted relationship between two or more variables. Clearly identify the independent variables (the variables being changed) and the dependent variables (the variables being measured), as well as the population you are studying. Review and revise your hypothesis as needed.  

An example of a research hypothesis in this format is as follows:  

“ If [athletes] follow [cold water showers daily], then their [endurance] increases.”  

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

You may have understood the characteristics of a good hypothesis . But note that a research hypothesis is not always confirmed; a researcher should be prepared to accept or reject the hypothesis based on the study findings.  

Research hypothesis checklist  

Following from above, here is a 10-point checklist for a good research hypothesis :  

• Testable: A research hypothesis should be able to be tested via experimentation or observation.  
• Specific: A research hypothesis should clearly state the relationship between the variables being studied.  
• Based on prior research: A research hypothesis should be based on existing knowledge and previous research in the field.  
• Falsifiable: A research hypothesis should be able to be disproven through testing.  
• Clear and concise: A research hypothesis should be stated in a clear and concise manner.  
• Logical: A research hypothesis should be logical and consistent with current understanding of the subject.  
• Relevant: A research hypothesis should be relevant to the research question and objectives.  
• Feasible: A research hypothesis should be feasible to test within the scope of the study.  
• Reflects the population: A research hypothesis should consider the population or sample being studied.  
• Uncomplicated: A good research hypothesis is written in a way that is easy for the target audience to understand.  

By following this research hypothesis checklist , you will be able to create a research hypothesis that is strong, well-constructed, and more likely to yield meaningful results.  

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

A null hypothesis states that there is no change in the dependent variable due to changes to the independent variable. This means that the results are due to chance and are not significant. A null hypothesis is denoted as H0 and is stated as the opposite of what the alternative hypothesis states.   

Example: “ The newly identified virus is not zoonotic .”  

2. Alternative hypothesis:

This states that there is a significant difference or relationship between the variables being studied. It is denoted as H1 or Ha and is usually accepted or rejected in favor of the null hypothesis.  

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

This specifies the direction of the relationship or difference between variables; therefore, it tends to use terms like increase, decrease, positive, negative, more, or less.   

Example: “ The inclusion of intervention X decreases infant mortality compared to the original treatment .”   

4. Non-directional hypothesis:

While it does not predict the exact direction or nature of the relationship between the two variables, a non-directional hypothesis states the existence of a relationship or difference between variables but not the direction, nature, or magnitude of the relationship. A non-directional hypothesis may be used when there is no underlying theory or when findings contradict previous research.  

Example, “ Cats and dogs differ in the amount of affection they express .”  

5. Simple hypothesis :

A simple hypothesis only predicts the relationship between one independent and another independent variable.  

Example: “ Applying sunscreen every day slows skin aging .”  

6 . Complex hypothesis :

A complex hypothesis states the relationship or difference between two or more independent and dependent variables.   

Example: “ Applying sunscreen every day slows skin aging, reduces sun burn, and reduces the chances of skin cancer .” (Here, the three dependent variables are slowing skin aging, reducing sun burn, and reducing the chances of skin cancer.)  

7. Associative hypothesis:  

An associative hypothesis states that a change in one variable results in the change of the other variable. The associative hypothesis defines interdependency between variables.  

Example: “ There is a positive association between physical activity levels and overall health .”  

8 . Causal hypothesis:

A causal hypothesis proposes a cause-and-effect interaction between variables.  

Example: “ Long-term alcohol use causes liver damage .”  

Note that some of the types of research hypothesis mentioned above might overlap. The types of hypothesis chosen will depend on the research question and the objective of the study.  

Research hypothesis examples  

Here are some good research hypothesis examples :  

“The use of a specific type of therapy will lead to a reduction in symptoms of depression in individuals with a history of major depressive disorder.”  

“Providing educational interventions on healthy eating habits will result in weight loss in overweight individuals.”  

“Plants that are exposed to certain types of music will grow taller than those that are not exposed to music.”  

“The use of the plant growth regulator X will lead to an increase in the number of flowers produced by plants.”  

Characteristics that make a research hypothesis weak are unclear variables, unoriginality, being too general or too vague, and being untestable. A weak hypothesis leads to weak research and improper methods.   

Some bad research hypothesis examples (and the reasons why they are “bad”) are as follows:  

“This study will show that treatment X is better than any other treatment . ” (This statement is not testable, too broad, and does not consider other treatments that may be effective.)  

“This study will prove that this type of therapy is effective for all mental disorders . ” (This statement is too broad and not testable as mental disorders are complex and different disorders may respond differently to different types of therapy.)  

“Plants can communicate with each other through telepathy . ” (This statement is not testable and lacks a scientific basis.)  

Importance of testable hypothesis  

If a research hypothesis is not testable, the results will not prove or disprove anything meaningful. The conclusions will be vague at best. A testable hypothesis helps a researcher focus on the study outcome and understand the implication of the question and the different variables involved. A testable hypothesis helps a researcher make precise predictions based on prior research.  

To be considered testable, there must be a way to prove that the hypothesis is true or false; further, the results of the hypothesis must be reproducible.  

Frequently Asked Questions (FAQs) on research hypothesis  

1. What is the difference between research question and research hypothesis ?  

A research question defines the problem and helps outline the study objective(s). It is an open-ended statement that is exploratory or probing in nature. Therefore, it does not make predictions or assumptions. It helps a researcher identify what information to collect. A research hypothesis , however, is a specific, testable prediction about the relationship between variables. Accordingly, it guides the study design and data analysis approach.

2. When to reject null hypothesis ?

A null hypothesis should be rejected when the evidence from a statistical test shows that it is unlikely to be true. This happens when the test statistic (e.g., p -value) is less than the defined significance level (e.g., 0.05). Rejecting the null hypothesis does not necessarily mean that the alternative hypothesis is true; it simply means that the evidence found is not compatible with the null hypothesis.  

3. How can I be sure my hypothesis is testable?  

A testable hypothesis should be specific and measurable, and it should state a clear relationship between variables that can be tested with data. To ensure that your hypothesis is testable, consider the following:  

• Clearly define the key variables in your hypothesis. You should be able to measure and manipulate these variables in a way that allows you to test the hypothesis.  
• The hypothesis should predict a specific outcome or relationship between variables that can be measured or quantified.   
• You should be able to collect the necessary data within the constraints of your study.  
• It should be possible for other researchers to replicate your study, using the same methods and variables.   
• Your hypothesis should be testable by using appropriate statistical analysis techniques, so you can draw conclusions, and make inferences about the population from the sample data.  
• The hypothesis should be able to be disproven or rejected through the collection of data.  

4. How do I revise my research hypothesis if my data does not support it?  

If your data does not support your research hypothesis , you will need to revise it or develop a new one. You should examine your data carefully and identify any patterns or anomalies, re-examine your research question, and/or revisit your theory to look for any alternative explanations for your results. Based on your review of the data, literature, and theories, modify your research hypothesis to better align it with the results you obtained. Use your revised hypothesis to guide your research design and data collection. It is important to remain objective throughout the process.  

5. I am performing exploratory research. Do I need to formulate a research hypothesis?  

As opposed to “confirmatory” research, where a researcher has some idea about the relationship between the variables under investigation, exploratory research (or hypothesis-generating research) looks into a completely new topic about which limited information is available. Therefore, the researcher will not have any prior hypotheses. In such cases, a researcher will need to develop a post-hoc hypothesis. A post-hoc research hypothesis is generated after these results are known.  

6. How is a research hypothesis different from a research question?

A research question is an inquiry about a specific topic or phenomenon, typically expressed as a question. It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis.

7. Can a research hypothesis change during the research process?

Yes, research hypotheses can change during the research process. As researchers collect and analyze data, new insights and information may emerge that require modification or refinement of the initial hypotheses. This can be due to unexpected findings, limitations in the original hypotheses, or the need to explore additional dimensions of the research topic. Flexibility is crucial in research, allowing for adaptation and adjustment of hypotheses to align with the evolving understanding of the subject matter.

8. How many hypotheses should be included in a research study?

The number of research hypotheses in a research study varies depending on the nature and scope of the research. It is not necessary to have multiple hypotheses in every study. Some studies may have only one primary hypothesis, while others may have several related hypotheses. The number of hypotheses should be determined based on the research objectives, research questions, and the complexity of the research topic. It is important to ensure that the hypotheses are focused, testable, and directly related to the research aims.

9. Can research hypotheses be used in qualitative research?

Yes, research hypotheses can be used in qualitative research, although they are more commonly associated with quantitative research. In qualitative research, hypotheses may be formulated as tentative or exploratory statements that guide the investigation. Instead of testing hypotheses through statistical analysis, qualitative researchers may use the hypotheses to guide data collection and analysis, seeking to uncover patterns, themes, or relationships within the qualitative data. The emphasis in qualitative research is often on generating insights and understanding rather than confirming or rejecting specific research hypotheses through statistical testing.

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Writing Guides  /  How to Write a Hypothesis w/ Strong Examples

How to Write a Hypothesis w/ Strong Examples

A hypothesis is a guess about what’s going to happen.  In research, the hypothesis is what you the researcher expects the outcome of an experiment, a study, a test, or a program to be.  It is a belief based on the evidence you have before you, the reasoning of your mind, and what prior experience tells you.  The hypothesis is not 100% guaranteed—that’s why there are different kinds of hypotheses.  In this article, we’ll explain what those are when they should be used.  So let’s dive in!

What is a Hypothesis / Definition

A hypothesis is like a bet:  you size things up and tell your mates exactly what you think is going to happen with respect to X, Y, Z.  It can also be like an explanation for a phenomenon, or a logical prediction of a possible causal correlation among multiple factors. In science—or, really, in any field, a hypothesis is used as a basis for further investigation.  For example, many qualitative or exploratory studies are conducted just so that the researcher in the end can formulate a hypothesis after all the data is collected an analyzed.

In short, it is an educated guess, based on existing knowledge or observation.  It is a way of proposing a possible explanation for a relationship between variables.

One thing to remember is this:  the key characteristic of a hypothesis is that it must be testable and potentially falsifiable. This means that it should be possible to design an experiment or observation that could potentially prove the hypothesis wrong.  That is a very important point to keep in mind.

For that reason, hypotheses are usually only formulated after conducting a preliminary review of existing literature, observations, or after obtaining a general understanding of the subject area. They are not random guesses.  They are grounded in some form of evidence or understanding of the phenomena being studied. The formulation of a hypothesis is a big step in the scientific method, as it defines the focus and direction of the research.  A lot of time is often spent simply on developing a good hypothesis.

Why?  A well-constructed hypothesis not only proposes an explanation for an observation but also often predicts measurable and testable outcomes. It is not merely a question, but rather a statement that includes a clear explanation or prediction. For example, rather than asking “Does temperature affect the growth of bacteria?”, a hypothesis would be something like this:  “If the temperature increases, then the growth rate of bacteria will increase.”  It is clear, measurable, testable, and potentially falsifiable.

In the scientific community, a hypothesis is respected when it has the potential to advance knowledge, regardless of whether testing proves it to be true or false. The process of testing, refining, or nullifying hypotheses through experimentation and observation is part of what research is all about.

 Different types of Hypotheses

Hypotheses can be categorized into several types.  Each type has a unique purpose in scientific research.  Understanding these types is helpful for formulating a hypothesis that is appropriate to your specific research question. The main types of hypotheses include the following:

• Simple Hypothesis : This formulates a relationship between two variables, one independent and one dependent. It is straightforward and concise, making it easy to test.  It is most often used in basic scientific experiments where the aim is to investigate the relationship between two variables, such as in laboratory experiments or controlled field studies.
• Complex Hypothesis : Unlike the simple hypothesis, a complex hypothesis involves multiple independent and dependent variables. It is used in studies that are looking at several factors simultaneously, where there is an interplay of multiple variables. These are common in fields like social sciences, behavioral studies, and large-scale environmental research.
• Directional Hypothesis : This type predicts the nature of the effect of the independent variable on the dependent variable. It specifies the direction of the expected relationship.  It tends to be used studies where prior research or theory has already suggested a specific direction of influence or effect, such as in clinical trials or in studies testing theoretical models.
• Non-directional Hypothesis : In contrast to the directional hypothesis, a non-directional hypothesis does not specify the direction of the relationship. It simply suggests that there is a relationship between variables without stating whether it is positive or negative.  It is often used in exploratory research where the direction of the relationship is not known, such as in early-stage psychological research or when studying new phenomena.
• Null Hypothesis : The null hypothesis states that there is no relationship between the variables being studied. It is a default position that assumes no effect until evidence suggests otherwise.  It is also a fundamental aspect of virtually all quantitative research, serving as the hypothesis that there is no effect or no difference, against which the alternative hypothesis is tested.
• Associative and Causal Hypotheses : Associative hypotheses propose a relationship between variables where changes in one variable correspond with changes in another.  They are common in observational studies, such as epidemiological research or surveys, where the goal is to identify correlations between variables.  Causal hypotheses go a step further by suggesting that one variable causes the change in the other.  They are used in experimental research designed to determine cause-and-effect relationships, such as randomized controlled trials in medical research or controlled experiments in psychology.

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How to Write a Good Hypothesis

Writing a good hypothesis is definitely a good skill to have in scientific research. But it is also one that you can definitely learn with some practice if you don’t already have it.  Just keep in mind that the hypothesis is what sets the stage for the entire investigation.  It guides the methods and analysis.  Everything you do in research stems from your research question and hypothesis.

Here are four essential steps to follow when crafting a hypothesis:

• Start with a Research Question

Every hypothesis begins with a clear, focused research question. This question should arise from a review of existing literature, some observations you have made in the field, or an information gap that is apparent in current knowledge. The question should be specific and researchable.  For example, instead of a broad question like “What affects plant growth?”, a more specific question would be “How does the amount of water affect the growth of sunflowers?”  This is a specific question, and sets up a stage for a perfect hypothesis.

How did you develop the question?  Easy.  You simply took a broad view first, and then began looking more closely.  You looked into the subject matter.  And, as with anything, the more you look into it, the more likely you are to have questions.  So, the most important step here is to get a sense of your subject.  The more you learn about it, the more likely you will be to have a good research question.  Ask yourself:  what about this subject would I like to know more about?  It helps if you have a genuine interest in the topic!  Say, for example, you want to know more about cryptocurrency security or scalability:  wouldn’t you start asking questions about how to achieve either?  And wouldn’t you need to know a bit about the topic before you can ask the right question?  Of course!  Apply that same logic to whatever subject you are researching and your research question will appear rather quickly.

• Do Preliminary Research

Before formulating your hypothesis, you of course should conduct preliminary research. This involves reviewing existing literature, understanding the current state of knowledge in the field, doing some critical thinking on the subject, and considering any existing theories and findings that might be relevant. This preliminary research helps in developing an educated guess.  If you do your background research well, your hypothesis will be grounded in existing knowledge.

This is basically the step that comes after you ask your research question but before you make a prediction about the subject matter.  Just like if you went to a racetrack and wanted to place a bet on a horse, you would research the horses, the owners, the teams, and make an educated guess about which one is most likely to win, doing preliminary research is the same:  you want to become very familiar with the topic—know it inside and out.  Then you will have everything you need to formulate your hypothesis.

• Formulate the Hypothesis

Based on your research question and preliminary research, now you can create your hypothesis. A good hypothesis should be clear, concise, and testable. It typically takes a statement form, predicting a potential outcome or relationship between variables. Make sure that your hypothesis is focused and answers your research question.  For example, a hypothesis for the research question stated above might be:   “If sunflower plants are watered with varying amounts of water, then those watered more frequently will grow taller due to better hydration.”

Keep in mind that when you reach the stage of formulating your hypothesis, you are essentially ready to make a statement that can be tested through research or experimentation. Your hypothesis should be as precise as possible. Don’t ever use ambiguous language in your hypothesis.  Also, you should be very specific about the variables involved and the expected relationship between them (if applicable).  For example, let’s look at the hypothesis we generated above:  “If sunflower plants are watered with varying amounts of water, then those watered more frequently will grow taller due to better hydration.”  We have clearly identified the variables (frequency of watering and plant growth height) and the expected outcome.

But what else should your hypothesis do?  Well, when we say it should address your research question, we mean it should be a logical extension of the question and your preliminary research.  If your research question is about the effect of watering frequency on sunflower growth, your hypothesis should specifically predict how these two variables are related.  It should not get into the types of soil, sunshine, temperature, or other variables unless these were brought up specifically in your research question.

Above all, you want your hypothesis to make a prediction. This means stating an expected outcome based on your understanding of the subject. The prediction is what will be tested through experiments or observations.

• Ensure Testability and Falsifiability

An important aspect of a good hypothesis is that it must be testable and potentially falsifiable. This means you should be able to conduct experiments or make observations that can support or refute the hypothesis. Avoid vague or broad statements that cannot be empirically tested.  Also, make sure that your hypothesis is potentially falsifiable; i.e., there should exist the possibility that it can be proven wrong.  For example, a hypothesis like “Sunflower plants need water to grow” is not falsifiable, as it is already a well-established fact.  But a hypothesis regarding frequency or amount of watering does have the potential to be nullified.

Therefore, keep that in mind during this step:  for a hypothesis to be testable, there must be a way to conduct an experiment or make observations that can confirm or disprove it. This means you should be able to measure or observe the variables involved. In the sunflower example, you can measure plant growth and control the frequency of watering very easily.  This is precisely what makes the hypothesis testable.

Another important point is falsifiability, as this is what separates scientific hypotheses from non-scientific ones.  If it doesn’t have the potential to be proven wrong, it’s not a hypothesis.  Being falsifiable doesn’t mean a hypothesis is false. It means that if the hypothesis is false, there is a way to demonstrate this. The potential for falsification is what allows researchers to make scientific progress no matter the problem or field.

Also, don’t be vague.  Your hypothesis needs to be specific: hypotheses that are too vague or broad are not useful in research, as there is no way to test them.  For example, saying “Water affects plant growth” is too vague.  How does water affect growth?  Is it the amount, frequency, or type of water?  Such a hypothesis needs to be more specific to be testable.  See what we mean?

Remember:   A hypothesis does not need to be correct.  It just needs to be testable.  It is a starting point for investigation. The value of a hypothesis lies in its ability to be tested.  The results of that test are what can potentially contribute to the existing body of scientific knowledge, regardless of whether the hypothesis is supported or refuted by the resulting data.

Hypothesis Examples

Simple hypothesis examples.

• Increasing the amount of natural light in a classroom will improve students’ test scores.
• Drinking at least eight glasses of water a day reduces the frequency of headaches in adults.
• Plant growth is faster when the plant is exposed to music for at least one hour per day.

Complex Hypothesis Examples

• Students’ academic performance is influenced by their study habits, family income, and the educational level of their parents.
• Employee productivity is affected by workplace environment, job satisfaction, and the level of personal stress the worker encounters both on the job and at home.
• The effectiveness of a weight loss program is dependent on the participant’s age, gender, and adherence to an appropriate diet plan.

Directional Hypothesis Examples

• Exposure to high levels of air pollution during pregnancy will increase the risk of asthma in children.
• A diet high in antioxidants will decrease the risk of heart disease in middle-aged adults.
• Regular physical exercise leads to a significant decrease in the symptoms of depression in adults.

Non-directional Hypothesis Examples

• There is a relationship between the amount of sleep a person gets and their level of stress.
• A change in classroom environment has an effect on student concentration.
• The introduction of ergonomics in the workplace environment impacts employee productivity.

Null Hypothesis Examples

• There is no significant difference in test scores between students who study in groups and those who study alone.
• Dietary changes have no effect on the improvement of symptoms in patients with type 2 diabetes.
• The new marketing strategy does not affect the sales numbers of the product.

Associative Hypothesis Examples

• There is an association between the number of hours spent on social media and the level of anxiety in teenagers.
• Daily consumption of green tea is associated with weight loss in adults.
• The frequency of public transport use correlates with the level of urban air pollution.

Causal Hypotheses Examples

• Implementing a school-based exercise program causes a reduction in obesity rates among children.
• High levels of job stress cause an increase in blood pressure.
• Smoking causes an increase in the risk of developing lung cancer.

In conclusion, understanding and effectively formulating a solid hypothesis is what scientific research and inquiry is all about—regardless of the type of work you’re doing.  It may be a simple, complex, directional, non-directional, null, associative, or causal hypothesis—no matter:  each type has its own specific purpose and guides the direction of a study in a different way. A simple hypothesis explores the relationship between two variables, while a complex hypothesis involves multiple variables. Directional hypotheses specify the expected direction of a relationship, whereas non-directional hypotheses do not. The null hypothesis, a fundamental aspect of statistical testing, posits no effect or relationship, serving as a baseline for analysis. Associative hypotheses explore correlations between variables, and causal hypotheses aim to establish cause-and-effect relationships.

The ability to craft a clear, concise, and testable hypothesis is important for any researcher. It is what shapes the course of the investigation.  It is also the backbone of the scientific method itself. A well-formulated hypothesis can lead to groundbreaking research or make significant contributions to knowledge in different fields.

As we have shown you with our examples, the hypothesis is more than a mere guess; it is an educated, testable prediction that guides you through the process of scientific discovery. When you master the art of hypothesis formulation, you can set off on your investigation with a clear roadmap and a clear sense of purpose.

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Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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• How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more variables . An independent variable is something the researcher changes or controls. A dependent variable is something the researcher observes and measures.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

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Step 1: ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2: Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalise more complex constructs.

Step 3: Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

Step 4: Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

Step 5: Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in if … then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

Step 6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis. The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

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Research Hypothesis In Psychology: Types, & Examples

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.

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A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

• A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
• It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
• A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
• Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
• For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
• Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

• Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

• Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
• However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

• Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
• Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
• Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
• Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
• Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

• The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
• The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

• Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
• Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
• Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
• Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
• Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
• Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
• Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
• Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

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How to Write a Research Hypothesis: Good & Bad Examples

What is a research hypothesis?

A research hypothesis is an attempt at explaining a phenomenon or the relationships between phenomena/variables in the real world. Hypotheses are sometimes called “educated guesses”, but they are in fact (or let’s say they should be) based on previous observations, existing theories, scientific evidence, and logic. A research hypothesis is also not a prediction—rather, predictions are ( should be) based on clearly formulated hypotheses. For example, “We tested the hypothesis that KLF2 knockout mice would show deficiencies in heart development” is an assumption or prediction, not a hypothesis. 

The research hypothesis at the basis of this prediction is “the product of the KLF2 gene is involved in the development of the cardiovascular system in mice”—and this hypothesis is probably (hopefully) based on a clear observation, such as that mice with low levels of Kruppel-like factor 2 (which KLF2 codes for) seem to have heart problems. From this hypothesis, you can derive the idea that a mouse in which this particular gene does not function cannot develop a normal cardiovascular system, and then make the prediction that we started with. 

What is the difference between a hypothesis and a prediction?

You might think that these are very subtle differences, and you will certainly come across many publications that do not contain an actual hypothesis or do not make these distinctions correctly. But considering that the formulation and testing of hypotheses is an integral part of the scientific method, it is good to be aware of the concepts underlying this approach. The two hallmarks of a scientific hypothesis are falsifiability (an evaluation standard that was introduced by the philosopher of science Karl Popper in 1934) and testability —if you cannot use experiments or data to decide whether an idea is true or false, then it is not a hypothesis (or at least a very bad one).

So, in a nutshell, you (1) look at existing evidence/theories, (2) come up with a hypothesis, (3) make a prediction that allows you to (4) design an experiment or data analysis to test it, and (5) come to a conclusion. Of course, not all studies have hypotheses (there is also exploratory or hypothesis-generating research), and you do not necessarily have to state your hypothesis as such in your paper. 

But for the sake of understanding the principles of the scientific method, let’s first take a closer look at the different types of hypotheses that research articles refer to and then give you a step-by-step guide for how to formulate a strong hypothesis for your own paper.

Types of Research Hypotheses

Hypotheses can be simple , which means they describe the relationship between one single independent variable (the one you observe variations in or plan to manipulate) and one single dependent variable (the one you expect to be affected by the variations/manipulation). If there are more variables on either side, you are dealing with a complex hypothesis. You can also distinguish hypotheses according to the kind of relationship between the variables you are interested in (e.g., causal or associative ). But apart from these variations, we are usually interested in what is called the “alternative hypothesis” and, in contrast to that, the “null hypothesis”. If you think these two should be listed the other way round, then you are right, logically speaking—the alternative should surely come second. However, since this is the hypothesis we (as researchers) are usually interested in, let’s start from there.

Alternative Hypothesis

If you predict a relationship between two variables in your study, then the research hypothesis that you formulate to describe that relationship is your alternative hypothesis (usually H1 in statistical terms). The goal of your hypothesis testing is thus to demonstrate that there is sufficient evidence that supports the alternative hypothesis, rather than evidence for the possibility that there is no such relationship. The alternative hypothesis is usually the research hypothesis of a study and is based on the literature, previous observations, and widely known theories. 

Null Hypothesis

The hypothesis that describes the other possible outcome, that is, that your variables are not related, is the null hypothesis ( H0 ). Based on your findings, you choose between the two hypotheses—usually that means that if your prediction was correct, you reject the null hypothesis and accept the alternative. Make sure, however, that you are not getting lost at this step of the thinking process: If your prediction is that there will be no difference or change, then you are trying to find support for the null hypothesis and reject H1. 

Directional Hypothesis

While the null hypothesis is obviously “static”, the alternative hypothesis can specify a direction for the observed relationship between variables—for example, that mice with higher expression levels of a certain protein are more active than those with lower levels. This is then called a one-tailed hypothesis. 

Another example for a directional one-tailed alternative hypothesis would be that 

H1: Attending private classes before important exams has a positive effect on performance. 

Your null hypothesis would then be that

H0: Attending private classes before important exams has no/a negative effect on performance.

Nondirectional Hypothesis

A nondirectional hypothesis does not specify the direction of the potentially observed effect, only that there is a relationship between the studied variables—this is called a two-tailed hypothesis. For instance, if you are studying a new drug that has shown some effects on pathways involved in a certain condition (e.g., anxiety) in vitro in the lab, but you can’t say for sure whether it will have the same effects in an animal model or maybe induce other/side effects that you can’t predict and potentially increase anxiety levels instead, you could state the two hypotheses like this:

H1: The only lab-tested drug (somehow) affects anxiety levels in an anxiety mouse model.

You then test this nondirectional alternative hypothesis against the null hypothesis:

H0: The only lab-tested drug has no effect on anxiety levels in an anxiety mouse model.

How to Write a Hypothesis for a Research Paper

Now that we understand the important distinctions between different kinds of research hypotheses, let’s look at a simple process of how to write a hypothesis.

Writing a Hypothesis Step:1

Ask a question, based on earlier research. Research always starts with a question, but one that takes into account what is already known about a topic or phenomenon. For example, if you are interested in whether people who have pets are happier than those who don’t, do a literature search and find out what has already been demonstrated. You will probably realize that yes, there is quite a bit of research that shows a relationship between happiness and owning a pet—and even studies that show that owning a dog is more beneficial than owning a cat ! Let’s say you are so intrigued by this finding that you wonder: 

What is it that makes dog owners even happier than cat owners? 

Let’s move on to Step 2 and find an answer to that question.

Writing a Hypothesis Step 2:

Formulate a strong hypothesis by answering your own question. Again, you don’t want to make things up, take unicorns into account, or repeat/ignore what has already been done. Looking at the dog-vs-cat papers your literature search returned, you see that most studies are based on self-report questionnaires on personality traits, mental health, and life satisfaction. What you don’t find is any data on actual (mental or physical) health measures, and no experiments. You therefore decide to make a bold claim come up with the carefully thought-through hypothesis that it’s maybe the lifestyle of the dog owners, which includes walking their dog several times per day, engaging in fun and healthy activities such as agility competitions, and taking them on trips, that gives them that extra boost in happiness. You could therefore answer your question in the following way:

Dog owners are happier than cat owners because of the dog-related activities they engage in.

Now you have to verify that your hypothesis fulfills the two requirements we introduced at the beginning of this resource article: falsifiability and testability . If it can’t be wrong and can’t be tested, it’s not a hypothesis. We are lucky, however, because yes, we can test whether owning a dog but not engaging in any of those activities leads to lower levels of happiness or well-being than owning a dog and playing and running around with them or taking them on trips.  

Writing a Hypothesis Step 3:

Make your predictions and define your variables. We have verified that we can test our hypothesis, but now we have to define all the relevant variables, design our experiment or data analysis, and make precise predictions. You could, for example, decide to study dog owners (not surprising at this point), let them fill in questionnaires about their lifestyle as well as their life satisfaction (as other studies did), and then compare two groups of active and inactive dog owners. Alternatively, if you want to go beyond the data that earlier studies produced and analyzed and directly manipulate the activity level of your dog owners to study the effect of that manipulation, you could invite them to your lab, select groups of participants with similar lifestyles, make them change their lifestyle (e.g., couch potato dog owners start agility classes, very active ones have to refrain from any fun activities for a certain period of time) and assess their happiness levels before and after the intervention. In both cases, your independent variable would be “ level of engagement in fun activities with dog” and your dependent variable would be happiness or well-being . 

Examples of a Good and Bad Hypothesis

Let’s look at a few examples of good and bad hypotheses to get you started.

Good Hypothesis Examples

Bad Hypothesis Examples

Tips for Writing a Research Hypothesis

If you understood the distinction between a hypothesis and a prediction we made at the beginning of this article, then you will have no problem formulating your hypotheses and predictions correctly. To refresh your memory: We have to (1) look at existing evidence, (2) come up with a hypothesis, (3) make a prediction, and (4) design an experiment. For example, you could summarize your dog/happiness study like this:

(1) While research suggests that dog owners are happier than cat owners, there are no reports on what factors drive this difference. (2) We hypothesized that it is the fun activities that many dog owners (but very few cat owners) engage in with their pets that increases their happiness levels. (3) We thus predicted that preventing very active dog owners from engaging in such activities for some time and making very inactive dog owners take up such activities would lead to an increase and decrease in their overall self-ratings of happiness, respectively. (4) To test this, we invited dog owners into our lab, assessed their mental and emotional well-being through questionnaires, and then assigned them to an “active” and an “inactive” group, depending on… 

Note that you use “we hypothesize” only for your hypothesis, not for your experimental prediction, and “would” or “if – then” only for your prediction, not your hypothesis. A hypothesis that states that something “would” affect something else sounds as if you don’t have enough confidence to make a clear statement—in which case you can’t expect your readers to believe in your research either. Write in the present tense, don’t use modal verbs that express varying degrees of certainty (such as may, might, or could ), and remember that you are not drawing a conclusion while trying not to exaggerate but making a clear statement that you then, in a way, try to disprove . And if that happens, that is not something to fear but an important part of the scientific process.

Similarly, don’t use “we hypothesize” when you explain the implications of your research or make predictions in the conclusion section of your manuscript, since these are clearly not hypotheses in the true sense of the word. As we said earlier, you will find that many authors of academic articles do not seem to care too much about these rather subtle distinctions, but thinking very clearly about your own research will not only help you write better but also ensure that even that infamous Reviewer 2 will find fewer reasons to nitpick about your manuscript. 

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Now that you know how to write a strong research hypothesis for your research paper, you might be interested in our free AI Proofreader , Wordvice AI, which finds and fixes errors in grammar, punctuation, and word choice in academic texts. Or if you are interested in human proofreading , check out our English editing services , including research paper editing and manuscript editing .

On the Wordvice academic resources website , you can also find many more articles and other resources that can help you with writing the other parts of your research paper , with making a research paper outline before you put everything together, or with writing an effective cover letter once you are ready to submit.

What Are Examples of a Hypothesis?

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A hypothesis is an explanation for a set of observations. Hypothesis examples can help you understand how this scientific method works.

Although you could state a scientific hypothesis in various ways, most hypotheses are either "If, then" statements or forms of the null hypothesis. The null hypothesis is sometimes called the "no difference" hypothesis. The null hypothesis is good for experimentation because it's simple to disprove. If you disprove a null hypothesis, that is evidence for a relationship between the variables you are examining.

Hypotheses Examples: Null

• All daisies have the same number of petals.
• Hyperactivity is unrelated to eating sugar.
• The number of pets in a household is unrelated to the number of people living in it.
• A person's preference for a shirt is unrelated to its color.

Hypotheses Examples: If, Then

• If you get at least 6 hours of sleep, you will do better on tests than if you get less sleep.
• If you drop a ball, it will fall toward the ground.
• If you drink coffee before going to bed, then it will take longer to fall asleep.
• If you cover a wound with a bandage, then it will heal with less scarring.

Improving a Hypothesis to Make It Testable

You may wish to revise your first hypothesis to make it easier to design an experiment to test. For example, let's say you have a bad breakout the morning after eating a lot of greasy food. You may wonder if there is a correlation between eating greasy food and getting pimples. You propose the hypothesis example:

Eating greasy food causes pimples.

Next, you need to design an experiment to test this hypothesis. Let's say you decide to eat greasy food every day for a week and record the effect on your face. Then, as a control, you'll avoid greasy food for the next week and see what happens. Now, this is not a good experiment because it does not take into account other factors such as hormone levels, stress, sun exposure, exercise, or any number of other variables that might conceivably affect your skin.

The problem is that you cannot assign cause to your effect . If you eat french fries for a week and suffer a breakout, can you definitely say it was the grease in the food that caused it? Maybe it was the salt. Maybe it was the potato. Maybe it was unrelated to diet. You can't prove your hypothesis. It's much easier to disprove a hypothesis.

So, let's restate the hypothesis to make it easier to evaluate the data:

Getting pimples is unaffected by eating greasy food.

So, if you eat fatty food every day for a week and suffer breakouts and then don't break out the week that you avoid greasy food, you can be pretty sure something is up. Can you disprove the hypothesis? Probably not, since it is so hard to assign cause and effect. However, you can make a strong case that there is some relationship between diet and acne.

If your skin stays clear for the entire test, you may decide to accept your hypothesis . Again, you didn't prove or disprove anything, which is fine

• Null Hypothesis Examples
• The Role of a Controlled Variable in an Experiment
• Random Error vs. Systematic Error
• What Is a Testable Hypothesis?
• What Are the Elements of a Good Hypothesis?
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• What Is a Hypothesis? (Science)
• Scientific Method Vocabulary Terms
• Scientific Method Flow Chart
• Understanding Simple vs Controlled Experiments
• Six Steps of the Scientific Method
• What Is an Experimental Constant?
• What Is the Difference Between a Control Variable and Control Group?
• What Is a Controlled Experiment?
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• DRY MIX Experiment Variables Acronym

What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By:  Derek Jansen (MBA)  | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis 101

• What is a hypothesis ?
• What is a research hypothesis (scientific hypothesis)?
• Requirements for a research hypothesis
• Definition of a research hypothesis
• The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

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Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.  

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell. 

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

17 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

Hi” best wishes to you and your very nice blog” 

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»    »    »    »  , A is a reasoned explanation that is not yet confirmed by the scientific method. It is standard practice to formulate a hypothesis as a starting point of research. This is then refuted, confirmed or reframed based on evidence. The following are illustrative examples of a hypothesis. Plants will grow faster in blue light as compared to red or green light. Regular watering can desalinate soil in a pot. Local air quality is better on weekends and holidays. Tennis balls bounce higher when they are cold. There is significant variation in the average amount of pollen in regular versus organic honey products purchased from a local supermarket. A solution of toothpaste and water will disinfect a surface. Water isn't flammable. The color of paint in classrooms doesn't influence student grades. Snow tires will improve fuel efficiency in January when roads are cold and potentially snowy or icy. Seawater will evaporate more slowly than distilled water. Listening to pop music increases performance for tasks. Inspirational stories can increase the self-reported happiness of people. Consumers prefer green pencils to yellow or brown. A novice chess player will improve more with 4 hours of practice games than with 4 hours of theory and study. The lead content of ceramic cups will vary by color. Batteries last longer at room temperature as compared to subzero temperatures. For adults, reaction time decreases with age. Carrots grown organically in a home garden will have more vitamin A than those purchased at a local supermarket. Dogs can see the color blue but not red. People will prefer the brand of cola with the highest sugar content. Watching superhero movies makes people more confident in their own physical strength. Classrooms are noisier when the teacher has left the room. Fonts influence essay grading. Question framing influences opinion polls. Computers have higher performance in a cold room. People can't differentiate between the taste of distilled water, mineral water and tap water. Bamboo has a higher strength to weight ratio than steel. People feel that time moves slower when they study as compared to playing video games. On average, non-fiction books will have more pages than fiction. Mindcraft zombies are capable of spatial reasoning. Paper can be made waterproof with beeswax. Consumption of sugary beverages has a negative influence on reading comprehension scores. People have greater self-reported happiness on sunny days as compared to rainy days. Longer morning commutes cause workers to report lower job satisfaction. Coders working from home will produce more lines of code per day with fewer defects per line. Shrimps in an aquarium do not demonstrate social behavior. Traditionally, a hypothesis has to be to conform to the scientific method. Falsifiability means that a test exists that can prove the hypothesis wrong. Falsifiability places the burden of proof on those who make claims. For example, "Aliens exist" isn't falsifiable because disproving this statement would require you to search every inch of the entire universe for life. This can be easily converted to the falsifiable "Aliens don't exist" whereby you can disprove the hypothesis by discovering a single alien. It should be noted that the falsifiability criterion is the subject of much debate, particularly in fields such as where theories are often constructed with logic such as induction. Likewise, several well known concepts that people think of as solid science are arguably not falsifiable. For example, "survival of the fittest" isn't particularly falsifiable as it's difficult to prove that some current species is less "fit" that than all other species that have gone extinct . The examples above include science, social sciences, engineering and other disciplines such as business. There is no need to outline the details of an experiment in the hypothesis but it is necessary to be specific to what you actually intend to prove or disprove without being overly broad. It is tempting to frame a hypothesis as a question but it must be declarative as in the examples above. It feels strategy to make a declaration when you don't know if it is true. However, this is the point of a hypothesis to bravely state something that might be true or might be false. It is invalid to change your initial hypothesis in the midst of research as this is an important artifact that indicates where results were expected based on rational thought or unexpected and possibly a random correlation. Research without a hypothesis such as trying to find a pattern in data is likely to confuse correlation and causation. A common type of research fraud, is to automatically look for patterns in datasets and then fit a hypothesis to this pattern. This is known as . Darwin defined natural selection as "better designed for an immediate, local environment" if is defined in this way, is unfalsifiable . However, modern biology tends to define fitness in terms of reproductive success. In this case, can be accused of along the lines of "those with the most reproductive success have the most reproductive success." This is the complete list of articles we have written about experiments. Experiments Experiment Variables Independent variables, dependent variable. » Experiment Controls Scientific control, extraneous variables, control group, negative control vs positive control, negative control, research skills, soft science, words to describe science, words to describe evidence, experiment design, science words, research design, new articles. 13 Different Types of Hypothesis 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] Learn about our Editorial Process There are 13 different types of hypothesis. These include simple, complex, null, alternative, composite, directional, non-directional, logical, empirical, statistical, associative, exact, and inexact. A hypothesis can be categorized into one or more of these types. However, some are mutually exclusive and opposites. Simple and complex hypotheses are mutually exclusive, as are direction and non-direction, and null and alternative hypotheses. Below I explain each hypothesis in simple terms for absolute beginners. These definitions may be too simple for some, but they’re designed to be clear introductions to the terms to help people wrap their heads around the concepts early on in their education about research methods . Types of Hypothesis Before you Proceed: Dependent vs Independent Variables A research study and its hypotheses generally examine the relationships between independent and dependent variables – so you need to know these two concepts: The independent variable is the variable that is causing a change. The dependent variable is the variable the is affected by the change. This is the variable being tested. Read my full article on dependent vs independent variables for more examples. Example: Eating carrots (independent variable) improves eyesight (dependent variable). 1. Simple Hypothesis A simple hypothesis is a hypothesis that predicts a correlation between two test variables: an independent and a dependent variable. This is the easiest and most straightforward type of hypothesis. You simply need to state an expected correlation between the dependant variable and the independent variable. You do not need to predict causation (see: directional hypothesis). All you would need to do is prove that the two variables are linked. Simple Hypothesis Examples Question Simple Hypothesis Do people over 50 like Coca-Cola more than people under 50? On average, people over 50 like Coca-Cola more than people under 50. According to national registries of car accident data, are Canadians better drivers than Americans? Canadians are better drivers than Americans. Are carpenters more liberal than plumbers? Carpenters are more liberal than plumbers. Do guitarists live longer than pianists? Guitarists do live longer than pianists. Do dogs eat more in summer than winter? Dogs do eat more in summer than winter. 2. Complex Hypothesis A complex hypothesis is a hypothesis that contains multiple variables, making the hypothesis more specific but also harder to prove. You can have multiple independent and dependant variables in this hypothesis. Complex Hypothesis Example Question Complex Hypothesis Do (1) age and (2) weight affect chances of getting (3) diabetes and (4) heart disease? (1) Age and (2) weight increase your chances of getting (3) diabetes and (4) heart disease. In the above example, we have multiple independent and dependent variables: Independent variables: Age and weight. Dependent variables: diabetes and heart disease. Because there are multiple variables, this study is a lot more complex than a simple hypothesis. It quickly gets much more difficult to prove these hypotheses. This is why undergraduate and first-time researchers are usually encouraged to use simple hypotheses. 3. Null Hypothesis A null hypothesis will predict that there will be no significant relationship between the two test variables. For example, you can say that “The study will show that there is no correlation between marriage and happiness.” A good way to think about a null hypothesis is to think of it in the same way as “innocent until proven guilty”[1]. Unless you can come up with evidence otherwise, your null hypothesis will stand. A null hypothesis may also highlight that a correlation will be inconclusive . This means that you can predict that the study will not be able to confirm your results one way or the other. For example, you can say “It is predicted that the study will be unable to confirm a correlation between the two variables due to foreseeable interference by a third variable .” Beware that an inconclusive null hypothesis may be questioned by your teacher. Why would you conduct a test that you predict will not provide a clear result? Perhaps you should take a closer look at your methodology and re-examine it. Nevertheless, inconclusive null hypotheses can sometimes have merit. Null Hypothesis Examples Question Null Hypothesis (H ) Do people over 50 like Coca-Cola more than people under 50? Age has no effect on preference for Coca-Cola. Are Canadians better drivers than Americans? Nationality has no effect on driving ability. Are carpenters more liberal than plumbers? There is no statistically significant difference in political views between carpenters and plumbers. Do guitarists live longer than pianists? There is no statistically significant difference in life expectancy between guitarists and pianists. Do dogs eat more in summer than winter? Time of year has no effect on dogs’ appetites. 4. Alternative Hypothesis An alternative hypothesis is a hypothesis that is anything other than the null hypothesis. It will disprove the null hypothesis. We use the symbol H A or H 1 to denote an alternative hypothesis. The null and alternative hypotheses are usually used together. We will say the null hypothesis is the case where a relationship between two variables is non-existent. The alternative hypothesis is the case where there is a relationship between those two variables. The following statement is always true: H 0 ≠ H A . Let’s take the example of the hypothesis: “Does eating oatmeal before an exam impact test scores?” We can have two hypotheses here: Null hypothesis (H 0 ): “Eating oatmeal before an exam does not impact test scores.” Alternative hypothesis (H A ): “Eating oatmeal before an exam does impact test scores.” For the alternative hypothesis to be true, all we have to do is disprove the null hypothesis for the alternative hypothesis to be true. We do not need an exact prediction of how much oatmeal will impact the test scores or even if the impact is positive or negative. So long as the null hypothesis is proven to be false, then the alternative hypothesis is proven to be true. 5. Composite Hypothesis A composite hypothesis is a hypothesis that does not predict the exact parameters, distribution, or range of the dependent variable. Often, we would predict an exact outcome. For example: “23 year old men are on average 189cm tall.” Here, we are giving an exact parameter. So, the hypothesis is not composite. But, often, we cannot exactly hypothesize something. We assume that something will happen, but we’re not exactly sure what. In these cases, we might say: “23 year old men are not on average 189cm tall.” We haven’t set a distribution range or exact parameters of the average height of 23 year old men. So, we’ve introduced a composite hypothesis as opposed to an exact hypothesis. Generally, an alternative hypothesis (discussed above) is composite because it is defined as anything except the null hypothesis. This ‘anything except’ does not define parameters or distribution, and therefore it’s an example of a composite hypothesis. 6. Directional Hypothesis A directional hypothesis makes a prediction about the positivity or negativity of the effect of an intervention prior to the test being conducted. Instead of being agnostic about whether the effect will be positive or negative, it nominates the effect’s directionality. We often call this a one-tailed hypothesis (in contrast to a two-tailed or non-directional hypothesis) because, looking at a distribution graph, we’re hypothesizing that the results will lean toward one particular tail on the graph – either the positive or negative. Directional Hypothesis Examples Question Directional Hypothesis Does adding a 10c charge to plastic bags at grocery stores lead to changes in uptake of reusable bags? Adding a 10c charge to plastic bags in grocery stores will lead to an in uptake of reusable bags. Does a Universal Basic Income influence retail worker wages? Universal Basic Income retail worker wages. Does rainy weather impact the amount of moderate to high intensity exercise people do per week in the city of Vancouver? Rainy weather the amount of moderate to high intensity exercise people do per week in the city of Vancouver. Does introducing fluoride to the water system in the city of Austin impact number of dental visits per capita per year? Introducing fluoride to the water system in the city of Austin the number of dental visits per capita per year? Does giving children chocolate rewards during study time for positive answers impact standardized test scores? Giving children chocolate rewards during study time for positive answers standardized test scores. 7. Non-Directional Hypothesis A non-directional hypothesis does not specify the predicted direction (e.g. positivity or negativity) of the effect of the independent variable on the dependent variable. These hypotheses predict an effect, but stop short of saying what that effect will be. A non-directional hypothesis is similar to composite and alternative hypotheses. All three types of hypothesis tend to make predictions without defining a direction. In a composite hypothesis, a specific prediction is not made (although a general direction may be indicated, so the overlap is not complete). For an alternative hypothesis, you often predict that the even will be anything but the null hypothesis, which means it could be more or less than H 0 (or in other words, non-directional). Let’s turn the above directional hypotheses into non-directional hypotheses. Non-Directional Hypothesis Examples Question Non-Directional Hypothesis Does adding a 10c charge to plastic bags at grocery stores lead to changes in uptake of reusable bags? Adding a 10c charge to plastic bags in grocery stores will lead to a in uptake of reusable bags. Does a Universal Basic Income influence retail worker wages? Universal Basic Income retail worker wages. Does rainy weather impact the amount of moderate to high intensity exercise people do per week in the city of Vancouver? Rainy weather the amount of moderate to high intensity exercise people do per week in the city of Vancouver. Does introducing fluoride to the water system in the city of Austin impact number of dental visits per capita per year? Introducing fluoride to the water system in the city of Austin the number of dental visits per capita per year? Does giving children chocolate rewards during study time for positive answers impact standardized test scores? Giving children chocolate rewards during study time for positive answers standardized test scores. 8. Logical Hypothesis A logical hypothesis is a hypothesis that cannot be tested, but has some logical basis underpinning our assumptions. These are most commonly used in philosophy because philosophical questions are often untestable and therefore we must rely on our logic to formulate logical theories. Usually, we would want to turn a logical hypothesis into an empirical one through testing if we got the chance. Unfortunately, we don’t always have this opportunity because the test is too complex, expensive, or simply unrealistic. Here are some examples: Before the 1980s, it was hypothesized that the Titanic came to its resting place at 41° N and 49° W, based on the time the ship sank and the ship’s presumed path across the Atlantic Ocean. However, due to the depth of the ocean, it was impossible to test. Thus, the hypothesis was simply a logical hypothesis. Dinosaurs closely related to Aligators probably had green scales because Aligators have green scales. However, as they are all extinct, we can only rely on logic and not empirical data. 9. Empirical Hypothesis An empirical hypothesis is the opposite of a logical hypothesis. It is a hypothesis that is currently being tested using scientific analysis. We can also call this a ‘working hypothesis’. We can to separate research into two types: theoretical and empirical. Theoretical research relies on logic and thought experiments. Empirical research relies on tests that can be verified by observation and measurement. So, an empirical hypothesis is a hypothesis that can and will be tested. Raising the wage of restaurant servers increases staff retention. Adding 1 lb of corn per day to cows’ diets decreases their lifespan. Mushrooms grow faster at 22 degrees Celsius than 27 degrees Celsius. Each of the above hypotheses can be tested, making them empirical rather than just logical (aka theoretical). 10. Statistical Hypothesis A statistical hypothesis utilizes representative statistical models to draw conclusions about broader populations. It requires the use of datasets or carefully selected representative samples so that statistical inference can be drawn across a larger dataset. This type of research is necessary when it is impossible to assess every single possible case. Imagine, for example, if you wanted to determine if men are taller than women. You would be unable to measure the height of every man and woman on the planet. But, by conducting sufficient random samples, you would be able to predict with high probability that the results of your study would remain stable across the whole population. You would be right in guessing that almost all quantitative research studies conducted in academic settings today involve statistical hypotheses. Statistical Hypothesis Examples Human Sex Ratio. The most famous statistical hypothesis example is that of John Arbuthnot’s sex at birth case study in 1710. Arbuthnot used birth data to determine with high statistical probability that there are more male births than female births. He called this divine providence, and to this day, his findings remain true: more men are born than women. Lady Testing Tea. A 1935 study by Ronald Fisher involved testing a woman who believed she could tell whether milk was added before or after water to a cup of tea. Fisher gave her 4 cups in which one randomly had milk placed before the tea. He repeated the test 8 times. The lady was correct each time. Fisher found that she had a 1 in 70 chance of getting all 8 test correct, which is a statistically significant result. 11. Associative Hypothesis An associative hypothesis predicts that two variables are linked but does not explore whether one variable directly impacts upon the other variable. We commonly refer to this as “ correlation does not mean causation ”. Just because there are a lot of sick people in a hospital, it doesn’t mean that the hospital made the people sick. There is something going on there that’s causing the issue (sick people are flocking to the hospital). So, in an associative hypothesis, you note correlation between an independent and dependent variable but do not make a prediction about how the two interact. You stop short of saying one thing causes another thing. Associative Hypothesis Examples Sick people in hospital. You could conduct a study hypothesizing that hospitals have more sick people in them than other institutions in society. However, you don’t hypothesize that the hospitals caused the sickness. Lice make you healthy. In the Middle Ages, it was observed that sick people didn’t tend to have lice in their hair. The inaccurate conclusion was that lice was not only a sign of health, but that they made people healthy. In reality, there was an association here, but not causation. The fact was that lice were sensitive to body temperature and fled bodies that had fevers. 12. Causal Hypothesis A causal hypothesis predicts that two variables are not only associated, but that changes in one variable will cause changes in another. A causal hypothesis is harder to prove than an associative hypothesis because the cause needs to be definitively proven. This will often require repeating tests in controlled environments with the researchers making manipulations to the independent variable, or the use of control groups and placebo effects . If we were to take the above example of lice in the hair of sick people, researchers would have to put lice in sick people’s hair and see if it made those people healthier. Researchers would likely observe that the lice would flee the hair, but the sickness would remain, leading to a finding of association but not causation. Causal Hypothesis Examples Question Causation Hypothesis Correlation Hypothesis Does marriage cause baldness among men? Marriage causes stress which leads to hair loss. Marriage occurs at an age when men naturally start balding. What is the relationship between recreational drugs and psychosis? Recreational drugs cause psychosis. People with psychosis take drugs to self-medicate. Do ice cream sales lead to increase drownings? Ice cream sales cause increased drownings. Ice cream sales peak during summer, when more people are swimming and therefore more drownings are occurring. 13. Exact vs. Inexact Hypothesis For brevity’s sake, I have paired these two hypotheses into the one point. The reality is that we’ve already seen both of these types of hypotheses at play already. An exact hypothesis (also known as a point hypothesis) specifies a specific prediction whereas an inexact hypothesis assumes a range of possible values without giving an exact outcome. As Helwig [2] argues: “An “exact” hypothesis specifies the exact value(s) of the parameter(s) of interest, whereas an “inexact” hypothesis specifies a range of possible values for the parameter(s) of interest.” Generally, a null hypothesis is an exact hypothesis whereas alternative, composite, directional, and non-directional hypotheses are all inexact. See Next: 15 Hypothesis Examples This is introductory information that is basic and indeed quite simplified for absolute beginners. It’s worth doing further independent research to get deeper knowledge of research methods and how to conduct an effective research study. And if you’re in education studies, don’t miss out on my list of the best education studies dissertation ideas . [1] https://jnnp.bmj.com/content/91/6/571.abstract [2] http://users.stat.umn.edu/~helwig/notes/SignificanceTesting.pdf Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 10 Reasons you’re Perpetually Single Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 20 Montessori Toddler Bedrooms (Design Inspiration) Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 21 Montessori Homeschool Setups Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 101 Hidden Talents Examples 2 thoughts on “13 Different Types of Hypothesis” Wow! This introductionary materials are very helpful. I teach the begginers in research for the first time in my career. The given tips and materials are very helpful. Chris, thank you so much! Excellent materials! You’re more than welcome! If you want a pdf version of this article to provide for your students to use as a weekly reading on in-class discussion prompt for seminars, just drop me an email in the Contact form and I’ll get one sent out to you. When I’ve taught this seminar, I’ve put my students into groups, cut these definitions into strips, and handed them out to the groups. Then I get them to try to come up with hypotheses that fit into each ‘type’. You can either just rotate hypothesis types so they get a chance at creating a hypothesis of each type, or get them to “teach” their hypothesis type and examples to the class at the end of the seminar. Cheers, Chris Leave a Comment Cancel Reply Your email address will not be published. Required fields are marked * Have a language expert improve your writing Run a free plagiarism check in 10 minutes, generate accurate citations for free. Knowledge Base Hypothesis Testing | A Step-by-Step Guide with Easy Examples Published on November 8, 2019 by Rebecca Bevans . Revised on June 22, 2023. Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics . It is most often used by scientists to test specific predictions, called hypotheses, that arise from theories. There are 5 main steps in hypothesis testing: State your research hypothesis as a null hypothesis and alternate hypothesis (H o ) and (H a  or H 1 ). Collect data in a way designed to test the hypothesis. Perform an appropriate statistical test . Decide whether to reject or fail to reject your null hypothesis. Present the findings in your results and discussion section. Though the specific details might vary, the procedure you will use when testing a hypothesis will always follow some version of these steps. Table of contents Step 1: state your null and alternate hypothesis, step 2: collect data, step 3: perform a statistical test, step 4: decide whether to reject or fail to reject your null hypothesis, step 5: present your findings, other interesting articles, frequently asked questions about hypothesis testing. After developing your initial research hypothesis (the prediction that you want to investigate), it is important to restate it as a null (H o ) and alternate (H a ) hypothesis so that you can test it mathematically. The alternate hypothesis is usually your initial hypothesis that predicts a relationship between variables. The null hypothesis is a prediction of no relationship between the variables you are interested in. H 0 : Men are, on average, not taller than women. H a : Men are, on average, taller than women. Here's why students love Scribbr's proofreading services Discover proofreading & editing For a statistical test to be valid , it is important to perform sampling and collect data in a way that is designed to test your hypothesis. If your data are not representative, then you cannot make statistical inferences about the population you are interested in. There are a variety of statistical tests available, but they are all based on the comparison of within-group variance (how spread out the data is within a category) versus between-group variance (how different the categories are from one another). If the between-group variance is large enough that there is little or no overlap between groups, then your statistical test will reflect that by showing a low p -value . This means it is unlikely that the differences between these groups came about by chance. Alternatively, if there is high within-group variance and low between-group variance, then your statistical test will reflect that with a high p -value. This means it is likely that any difference you measure between groups is due to chance. Your choice of statistical test will be based on the type of variables and the level of measurement of your collected data . an estimate of the difference in average height between the two groups. a p -value showing how likely you are to see this difference if the null hypothesis of no difference is true. Based on the outcome of your statistical test, you will have to decide whether to reject or fail to reject your null hypothesis. In most cases you will use the p -value generated by your statistical test to guide your decision. And in most cases, your predetermined level of significance for rejecting the null hypothesis will be 0.05 – that is, when there is a less than 5% chance that you would see these results if the null hypothesis were true. In some cases, researchers choose a more conservative level of significance, such as 0.01 (1%). This minimizes the risk of incorrectly rejecting the null hypothesis ( Type I error ). Receive feedback on language, structure, and formatting Professional editors proofread and edit your paper by focusing on: Academic style Vague sentences Style consistency See an example The results of hypothesis testing will be presented in the results and discussion sections of your research paper , dissertation or thesis . In the results section you should give a brief summary of the data and a summary of the results of your statistical test (for example, the estimated difference between group means and associated p -value). In the discussion , you can discuss whether your initial hypothesis was supported by your results or not. In the formal language of hypothesis testing, we talk about rejecting or failing to reject the null hypothesis. You will probably be asked to do this in your statistics assignments. However, when presenting research results in academic papers we rarely talk this way. Instead, we go back to our alternate hypothesis (in this case, the hypothesis that men are on average taller than women) and state whether the result of our test did or did not support the alternate hypothesis. If your null hypothesis was rejected, this result is interpreted as “supported the alternate hypothesis.” These are superficial differences; you can see that they mean the same thing. You might notice that we don’t say that we reject or fail to reject the alternate hypothesis . This is because hypothesis testing is not designed to prove or disprove anything. It is only designed to test whether a pattern we measure could have arisen spuriously, or by chance. If we reject the null hypothesis based on our research (i.e., we find that it is unlikely that the pattern arose by chance), then we can say our test lends support to our hypothesis . But if the pattern does not pass our decision rule, meaning that it could have arisen by chance, then we say the test is inconsistent with our hypothesis . 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. Normal distribution Descriptive statistics Measures of central tendency Correlation coefficient Methodology Cluster sampling Stratified sampling Types of interviews Cohort study Thematic analysis Research bias Implicit bias Cognitive bias Survivorship bias Availability heuristic Nonresponse bias Regression to the mean Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance. A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question. A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data). Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship. Cite this Scribbr article If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator. Bevans, R. (2023, June 22). Hypothesis Testing | A Step-by-Step Guide with Easy Examples. Scribbr. Retrieved August 30, 2024, from https://www.scribbr.com/statistics/hypothesis-testing/ Is this article helpful? Rebecca Bevans Other students also liked, choosing the right statistical test | types & examples, understanding p values | definition and examples, what is your plagiarism score. Research Paper Guides Basics of Research Paper Writing How to Write a Hypothesis: Step-by-Step Guide and Examples Speech Topics Basics of Essay Writing Essay Topics Other Essays Main Academic Essays Research Paper Topics Miscellaneous Chicago/ Turabian Data & Statistics Methodology Admission Writing Tips Admission Advice Other Guides Student Life Studying Tips Understanding Plagiarism Academic Writing Tips Basics of Dissertation & Thesis Writing Essay Guides Formatting Guides Basics of Research Process Admission Guides Dissertation & Thesis Guides Table of contents Use our free Readability checker Each academic research revolves around specific statement or problem — a research hypothesis.  A hypothesis is a suggested prediction for a phenomenon or observed event, based on prior knowledge or research. It is a tentative statement that can be tested through further investigation and analysis. A hypothesis usually takes the form of a statement that suggests a relationship between two or more variables. Every research project, be it a a term paper, research paper or a dissertation, should begin with defining a hypothesis. While this may seem simple, in reality beginners face a lot of problems. This includes difficulty with formulating a hypothesis accurately and capturing the main idea. In this blog post, we will tell you how to write a hypothesis so it is accurate and correct. What Is a Research Hypothesis: Expanded Definition A research hypothesis is a statement or assumption that answers a question you asked earlier but haven't tested yet. In fact, this is basis of your work which you use to prove or reject your assumption. Major research projects most often deal with several hypotheses. These relate to various aspects of an issue under study. Thus, you will divide assumptions by research sectors and study them in a segmented manner. When making an assignment, one must work based on an existing theory and gained knowledge. One must also take into account that it must be testable. That is, it can be rejected or confirmed with methods of scientific research. Hypothesis example may look like this: In your work, you must prove or reject this hypothesis by providing survey results. Show some statistical analysis , study of reports and other processed data. Remeber that you can hire a paper writer who will integrate survey outcomes and conduct statistical analysis in your research paper hypothesis.  Variables in Hypotheses To make a qualitative guess, you should consider variables in your hypothesis. They can be divided into independent and dependent ones. In fact, you must establish causal relationship between two or more variables. Independent ( confounding variable ) is what researcher can control or change, i.e. initial condition. Dependent ( extraneous variable ) is what researcher studies. It is observed in created conditions. Before you start learning how to write an assignment with independent and dependent variables, you should define the main idea of your work. For example, you take an assumption that eating hedgehog meat reduces risk of cardiovascular disease. Independent variable is hedgehog meat consumption, which is cause. Improvement in cardiovascular health is a dependent variable – an intended effect. How to Write a Hypothesis: 5 Simple Writing Steps Novice researchers most frequently ask how to write a hypothesis statement. This is a complex process that includes compilation of laconic predictions. These are based on conducted experiments. We can support you in this task. We have developed 5 steps for researchers so they can write a high-quality and comprehensive assignment. Step 1. Generate a Question Before Writing Your Hypothesis At the first stage of writing a hypothesis for a research paper you must define a research question that you need to answer. It should be focused on particular problem. Try to make it specific and yet suitable for research within framework of your project. To write quality assignment, you must use 6 classic statements. Thus, you must clarify: who, what, where, when, why and how. You must make question understandable in terms of positioning problem. Example of correct hypothesis: Step 2. Gather Preliminary Research for Your Hypothesis Before writing a research hypothesis, conduct some preliminary research to find out if your assumption is working and can be proved. You will get the key insights through observations or experiments. You can also use results of your colleagues who have already studied this issue. Thus, you will build a concept with formulated variables. You will study them and identify relationships between them. Step 3. Write a Strong Hypothesis With results of preliminary preparation and research questions, you can study how to write a strong hypothesis . First of all, highlight the main testing problem. You must formulate it as briefly as possible. Try to avoid stretching statements in an attempt to make paper longer. Be as clear as possible, avoid vague judgments. For example: This is not good option. It is better to apply hypothesis in the form of: This is a clear sentence that is devoid of unnecessary details. It allows you to immediately see an expected effect. Get practical help in writing research paper if you wish for more quality. Step 4. Refine Your Research Hypothesis Make sure a hypothesis for a research proposal formulated correctly. You must check if it has following elements: Dependent and independent variables. An object or phenomenon for testing. Expected outcome of study that you plan to work through. This must be part of an experiment or an observation. This way, you will specify question under study. You also will be able to verify it if needed. That is, you will move from general to particular. Step 5. Write a Null Hypothesis You may need to write a null hypothesis. Why and when, you may ask? When you use this method for processing specific statistics. You should specify if you plan to prove your point on its basis. In fact, it is clear position that doesn’t establish links between variables. For example, this statement is null hypothesis: It is basis for presenting one's own opinion. It allows to build an evidence base stemming from researcher's evidence. What Is the Difference Between a Null Hypothesis and an Alternative Hypothesis To better understand how to write null and alternative hypothesis that will form backbone of study, examine testable statements. Based on results, null hypothesis is prepared. It is a statement with no connection between variables. At the same time, scientists usually work with an alternative hypothesis. Here, they have already found a connection between phenomena. Ever considered custom research paper writing service ? So, the above statement about frequency of doctor visits can be modified to research of:  Hypothesis Examples Quite often, researchers find it difficult to formulate basis for  writing a research paper . Therefore, some examples of hypothesis will be useful for them. This will correspond to if-then connections. With their help you will also briefly outline the main part of current research. We will help you in formulating an assignment and offer several working options: Tips on Writing a Hypothesis It’s difficult to start writing a hypothesis for a research proposal. Especially for aspiring academics! After all, it is important that an assignment is clear and specific. It must also be viable for further development. Here are some tips to help you formulate your statement: Analyze interesting aspects. Review current studies and problems on the selected topic. Highlight what you wanted to explore, perhaps it will be a concept close to your previous works. Clarify the details. Spend time on preliminary analysis. You must also highlight controversial aspects and contemporary issues. Sometimes, even well-researched phenomena can be promising. Focus on your own work. It’s always easier to continue than to start anew. At the same time, you might not have considered all the theses in the previous study. Make the variables clear. Avoid ambiguous statements. Sounds a bit difficult? College paper help is there for you. How to Write a Scientific Hypothesis: Final Thoughts So, if you've come this far, you should already know how to how to write a hypothesis step by step. Before starting writing, analyze the problem and the topic. You should highlight the thesis that can be developed further. We recommend going through the following steps: Define the question you expect to receive an answer to. Do some preliminary research. Write it strongly. Refine it with variables, subject and phenomenon, and expected result. Make a null hypothesis and consider a different option. Let our assistants write your hypothesis for you! Choose a paper writer to your liking, send them your requirements and get a great paper in no-time! Frequently Asked Questions About Writing a Good Hypothesis 1. how can i improve my hypothesis. To make the hypothesis working and of high quality, be sure you select both independent and dependent variables and add them to the statement. Examine the relationships of these elements. Think if you can prove them and explain them in further research. 2. Is there a maximum number of hypotheses that is allowed in one research paper? You can write as many hypotheses as you want for your paper, because it all depends on your view on the topic and the desire to develop it in several directions. The main thing is that your project shouldn't be overloaded with too many hypotheses and that you pay enough attention to each of them. 3. How do I test my hypothesis? It’s easy to test the statement before you write a hypothesis for a research proposal. Do an experiment: ask your question and try answering it. If you succeed, this assignment can be used for more detailed study. 4. How long is a hypothesis? While writing the hypothesis, you must make it as direct as possible and, at the same time, clear it of extraneous judgments. Typically, it's 20 words long. We don’t recommend exceeding this volume, so as not to face difficulties in interpretation. Joe Eckel is an expert on Dissertations writing. He makes sure that each student gets precious insights on composing A-grade academic writing. You may also like Words with Friends Cheat Wordle Solver Word Unscrambler Scrabble Dictionary Anagram Solver Wordscapes Answers Make Our Dictionary Yours Sign up for our weekly newsletters and get: Grammar and writing tips Fun language articles #WordOfTheDay and quizzes By signing in, you agree to our Terms and Conditions and Privacy Policy . We'll see you in your inbox soon. Hypothesis Examples: Different Types in Science and Research DESCRIPTION Woman in Bed Examples of Hypothesis SOURCE Karen Moskowitz / The Image Bank / Getty Images PERMISSION Used under Getty Images license A hypothesis is a stepping stone to proving a theory. There are numerous types of hypotheses that can be employed when seeking to prove a theory. Additionally, there are many hypothesis examples that can help you form your own hypothesis. What Is a Hypothesis? Simply put, a hypothesis is an idea that can be tested based on the evidence available. A concept or statement must be tested to be proven credible. This serves as a starting point for further investigation to prove the hypothesis by applying the scientific method . However, there are multiple variables that can affect the results, and therefore the idea must be tested multiple times. Parts of a Hypothesis: Independent and Dependent Variables Before diving into hypothesis examples, take a moment to understand independent and dependent variables . an independent variable stands on its own and is not changed by other variables the dependent variable depends on other factors The independent variable can cause a change in the dependent variable, but the dependent variable cannot cause a change in the independent variable. For example: How does the amount of makeup one applies affect how clear their skin is? Here, the independent variable is the makeup, and the dependent variable is the skin. The variables are important because they help determine the cause and effect. In this example, the hypothesis could be: The amount of makeup one wears correlates to how clear their skin is. Therefore, this hypothesis must be tested before it can be proven correct. Types of Hypotheses The most common forms of hypotheses are: Simple Hypothesis Complex Hypothesis Null Hypothesis Alternative Hypothesis Logical Hypothesis Empirical Hypothesis Statistical Hypothesis See how these types of hypotheses are created through examples. Simple Hypothesis Examples A simple hypothesis predicts the relationship between two variables: the independent variable and the dependent variable. This relationship is demonstrated through these examples. Drinking sugary drinks daily leads to being overweight. Smoking cigarettes daily leads to lung cancer. Getting at least 8 hours of sleep can make people more alert. Complex Hypothesis Examples A complex hypothesis describes a relationship between variables . However, it’s a relationship between two or more independent variables and two or more dependent variables. You can follow these examples to get a better understanding of a complex hypothesis. Adults who 1) drink sugary beverages on a daily basis and 2) have a family history of health issues are more likely to 1) become overweight and 2) develop diabetes or other health issues. Individuals that 1) smoke cigarettes and 2) live in large cities are more likely than others to have 1) respiratory problems and 2) an increased risk of cancer. Individuals who 1) get an average of eight or more hours of sleep and 2) have a balanced diet and schedule are more likely to 1) be alert during the day and 2) have more energy. Null Hypothesis Examples A null hypothesis, denoted by H 0 , proposes that two factors or groups are unrelated and that there is no difference between certain characteristics of a population or process. You must test the likelihood of the null hypothesis, in tandem with an alternative hypothesis, in order to disprove or discredit it. Some examples of a null hypothesis include: There is no significant change in a person’s health during the times when they drink green tea only or root beer only. There is no significant change in an individual’s work habits whether they get eight hours or nine hours of sleep. There is no significant change in the growth of a plant if one uses distilled water only or vitamin-rich water only to water it. Alternative Hypothesis Examples An alternative hypothesis, denoted by H 1 or H A , is a claim that is contradictory to the null hypothesis. Researchers will pair the alternative hypothesis with the null hypothesis in order to prove that there is no relation. If the null hypothesis is disproven, then the alternative hypothesis will be accepted. If the null hypothesis is not rejected, then the alternative hypothesis will not be accepted. Some examples of alternative hypotheses are: A person’s health improves during the times when they drink green tea only, as opposed to root beer only. Work habits improve during the times when one gets 8 hours of sleep only, as opposed to 9 hours of sleep only. The growth of the plant improved during the times when it received vitamin-rich water only, as opposed to distilled water only. Logical Hypothesis Examples A logical hypothesis is a proposed explanation using limited evidence. Generally, you want to turn a logical hypothesis into an empirical hypothesis, putting your theories or postulations to the test. In reference to these examples, there is currently no evidence to support these hypotheses. However, you can form a hypothesis based on the data available to draw a logical conclusion. Cacti experience more successful growth rates than tulips on Mars. Beings from Mars would not be able to breathe the air in Earth's atmosphere. Creatures found at the bottom of the ocean use anaerobic respiration rather than aerobic respiration. Empirical Hypothesis Examples An empirical hypothesis, or working hypothesis, comes to life when a theory is being put to the test using observation and experiment. It's no longer just an idea or notion. Rather, it is going through trial and error and perhaps changing around those independent variables. Roses watered with liquid Vitamin B grow faster than roses watered with liquid Vitamin E. Women taking vitamin E grow hair faster than those taking vitamin K. Thirsty rats find their way through a maze quicker if there is water at the end of the maze. In these examples, trial and error is leading to a series of findings. The results of these experiments can all be observed and proven over time. Statistical Hypothesis Examples A statistical hypothesis is an examination of a portion of a population or statistical model. In this type of analysis, you use statistical information from an area. For example, if you wanted to conduct a study on the life expectancy of people from Savannah, you would want to examine every single resident of Savannah. This is not practical. Therefore, you would conduct your research using a statistical hypothesis or a sample of Savannah's population. 50% of Savannah's population lives beyond the age of 70. 80% of the U.S. population gets a divorce because of irreconcilable differences. 45% of the poor in the U.S. are illiterate. Parameters of a Good Hypothesis For a hypothesis to be sound , hold tight to these tips. Step 1: Ask Yourself Questions Define the independent and dependent variables very specifically, and don't take on more than you can handle. Keep yourself laser-focused on one specific cause-and-effect theory. Step 2: Be Logical and Use Precise Language Keep your language clean and simple. State your hypothesis as concisely and to the point as possible. A hypothesis is usually written in a form where it proposes that, if something is done, then something else will occur. Usually, you’ll want to form your hypothesis as a statement, not a question. For example: If I raise the temperature of a cup of water, then the amount of sugar that can be dissolved in it will be increased. Step 3: Make Sure Your Hypothesis Is Testable Any hypothesis will need proof. Your audience will have to see evidence and reason to believe your statement. For example, I may want to drink root beer all day, not green tea. If you're going to make me change my ways, I need some sound reasoning and experimental proof — perhaps case studies of others who lost weight, cleared up their skin and had a marked improvement in their immunity by drinking green tea. Applying Your Hypothesis Hypotheses and research are the backbone of science. A hypothesis that is well-stated, founded in truth, and that can withstand extensive and repeated research and experimentation can lead to new discoveries and breakthroughs. Simple Hypothesis Ai generator. Venturing into the realm of scientific inquiry, the hypothesis stands as a beacon, guiding researchers towards answers. A simple hypothesis, crisp and to the point, paves the way for clear testing and straightforward results. But how does one draft such a potent statement? And what makes it so effective? Join us as we demystify the art of creating simple hypothesis statements and share invaluable tips to refine your approach.” What is an example of a Simple hypothesis statement? A simple hypothesis statement typically specifies a relationship or difference between two variables. Here’s an example: Caffeine Consumption and Alertness: Consuming caffeine increases a person’s alertness. In this example, the two variables are “caffeine consumption” and “alertness.” The hypothesis simply posits that there’s a direct relationship between these two variables, indicating that as one (caffeine consumption) increases, the other (alertness) does as well. 100 Simple Hypothesis Statement Examples Size: 166 KB Simple hypothesis statements act as the backbone of research, succinctly proposing a direct relationship or difference between two variables. These straightforward declarations pave the way for clear testing and results, offering a digestible insight into expected outcomes. Sunlight and Plant Growth : Exposure to sunlight increases plant growth. Reading and Vocabulary Expansion : Regular reading leads to an expanded vocabulary. Exercise and Heart Health : Engaging in exercise improves heart health. Sugar Intake and Energy Levels : Consuming sugar temporarily boosts energy levels. Hydration and Skin Health : Drinking water improves skin hydration. Meditation and Stress Reduction : Practicing meditation reduces stress levels. Music and Productivity : Listening to music enhances work productivity. Sleep Duration and Cognitive Functions : Getting adequate sleep improves cognitive functions. Fertilizer and Crop Yield : Using fertilizers increases crop yields. Probiotics and Gut Health : Consuming probiotics enhances gut health. Screen Time and Eye Strain : Prolonged screen time leads to increased eye strain. Social Media Usage and Loneliness : Frequent social media use is linked to feelings of loneliness. Carbon Emissions and Global Warming : Higher carbon emissions contribute to global warming. Vitamin C and Immunity : Consuming vitamin C boosts immune functions. Artificial Lights and Sleep Quality : Exposure to artificial lights at night reduces sleep quality. Yoga and Flexibility : Regular yoga practice increases flexibility. Chocolate Consumption and Mood : Eating chocolate elevates mood. Brushing and Dental Health : Regular brushing reduces dental cavities. Temperature and Metabolism Rate : Cold environments accelerate metabolism. Pet Ownership and Happiness : Having a pet contributes to increased happiness levels. Puzzles and Brain Activity : Solving puzzles activates brain functions. Green Tea and Weight Loss : Drinking green tea aids in weight loss. Salt Intake and Blood Pressure : Consuming high amounts of salt raises blood pressure. Indoor Plants and Air Quality : Having indoor plants improves air quality. Antibiotics and Bacterial Infections : Taking antibiotics reduces bacterial infections. Laughter and Endorphin Release : Engaging in laughter releases endorphins. Gaming and Hand-Eye Coordination : Playing video games improves hand-eye coordination. Washing Hands and Germ Spread : Regular hand washing reduces the spread of germs. Spicy Foods and Metabolism : Consuming spicy foods boosts metabolism. Journaling and Emotional Health : Maintaining a journal aids emotional well-being. Urban Areas and Noise Pollution : Urban regions are associated with higher noise pollution. Physical Activity and Bone Density : Regular physical activity strengthens bone density. Aloe Vera and Skin Soothing : Applying aloe vera soothes skin irritations. Alcohol Consumption and Reaction Time : Drinking alcohol slows down reaction time. Bird Watching and Patience : Engaging in bird watching cultivates patience. Cycling and Leg Strength : Regular cycling enhances leg muscle strength. Public Speaking and Confidence : Practicing public speaking boosts confidence. Dancing and Cardiovascular Health : Dancing regularly improves cardiovascular health. Acupuncture and Pain Relief : Undergoing acupuncture reduces pain. Caloric Restriction and Lifespan : Reducing caloric intake is linked to extended lifespan. Olive Oil and Heart Health : Consuming olive oil promotes heart health. Mindfulness and Attention Span : Practicing mindfulness increases attention span. Bilingualism and Cognitive Flexibility : Being bilingual enhances cognitive flexibility. Heavy Metals and Water Toxicity : Presence of heavy metals increases water toxicity. Mountain Climbing and Stamina : Engaging in mountain climbing builds stamina. Urbanization and Wildlife Displacement : Increased urbanization leads to wildlife displacement. Mentoring and Career Progression : Having a mentor accelerates career progression. Organic Farming and Soil Health : Practicing organic farming enhances soil health. Red Wine and Antioxidant Intake : Consuming red wine increases antioxidant intake. Studying Abroad and Cultural Awareness : Studying abroad enhances cultural awareness. E-books and Reading Duration : Using e-books extends reading duration. Swimming and Lung Capacity : Regular swimming increases lung capacity. Deforestation and Carbon Dioxide Levels : Deforestation raises carbon dioxide levels. Fast Food and Obesity : Frequent fast food consumption is linked to obesity. Classical Music and Infant Sleep : Playing classical music improves infant sleep duration. Microplastics and Marine Pollution : The presence of microplastics heightens marine pollution. Afforestation and Rainfall : Increasing forest cover can lead to higher rainfall. Gratitude Journaling and Positivity : Maintaining a gratitude journal boosts feelings of positivity. Singing and Lung Function : Regular singing enhances lung function. Noise Pollution and Stress : Exposure to noise pollution increases stress levels. Rural Living and Mental Peace : Living in rural areas promotes mental peace. Soft Drinks and Bone Density : Consuming soft drinks decreases bone density. Travel and Open-mindedness : Traveling frequently fosters open-mindedness. Digital Learning and Student Engagement : Digital learning tools increase student engagement. Plastic Usage and Land Pollution : Increased plastic usage contributes to land pollution. Stretching and Muscle Flexibility : Daily stretching improves muscle flexibility. Wine Consumption and Gut Health : Moderate wine consumption benefits gut health. Homework and Academic Achievement : Regular homework boosts academic achievement. Raw Foods and Digestive Health : Consuming raw foods aids digestive health. High Heels and Posture : Wearing high heels affects posture negatively. Therapy and Mental Well-being : Engaging in therapy promotes mental well-being. Fast Charging and Battery Lifespan : Using fast charging can reduce battery lifespan. Pottery and Hand Dexterity : Practicing pottery improves hand dexterity. Group Study and Retention : Studying in groups enhances information retention. Red Meat and Cholesterol Levels : Consuming red meat increases cholesterol levels. Kombucha and Gut Flora : Drinking kombucha benefits gut flora. Night Driving and Accident Risk : Driving at night increases the risk of accidents. Karaoke and Social Bonding : Engaging in karaoke fosters social bonding. Balanced Diet and Energy Levels : Following a balanced diet boosts energy levels. Multitasking and Task Efficiency : Multitasking reduces task efficiency. Gardening and Stress Relief : Regular gardening acts as a stress reliever. Digital Detox and Sleep Quality : Undertaking a digital detox improves sleep quality. Massage and Muscle Relaxation : Getting massages aids muscle relaxation. Animal Therapy and Emotional Healing : Engaging with animals accelerates emotional healing. Crafting and Creativity : Regular crafting activities enhance creativity. Organ Donation and Life-saving : Organ donation can be life-saving. Bamboo Products and Sustainability : Using bamboo products promotes sustainability. Weight Training and Muscle Mass : Engaging in weight training increases muscle mass. Fermented Foods and Digestion : Consuming fermented foods aids digestion. Outdoor Activities and Vitamin D Levels : Engaging in outdoor activities boosts Vitamin D levels. Smoking and Lung Health : Regular smoking deteriorates lung health. Feedback and Performance Improvement : Receiving feedback improves performance. Adventure Sports and Risk-taking Ability : Engaging in adventure sports enhances risk-taking ability. Coding and Logical Thinking : Learning to code promotes logical thinking. Chocolate and Antioxidant Levels : Eating dark chocolate boosts antioxidant levels. Vegetarian Diet and Heart Health : Following a vegetarian diet improves heart health. Deep Breathing and Relaxation : Practicing deep breathing induces relaxation. Natural Light and Productivity : Exposure to natural light enhances productivity. Green Spaces and Mental Health : Access to green spaces boosts mental well-being. Recycling and Resource Conservation : Regular recycling promotes resource conservation. Simple Hypothesis Statement Examples for Kids Kids often view the world with endless curiosity. Simplified hypotheses allow them to test their surroundings and grasp scientific methods. These Hypothesis for kids statements, tailored for young inquisitors, provide clear cause-and-effect scenarios to foster their learning. Ice Melting : Placing ice in the sun will make it melt faster than in the shade. Plant Growth : Plants kept near the window grow taller than plants in dark corners. Pet’s Activity : Dogs are more active during the day than at night. Toy Durability : Soft toys tear more easily than hard plastic toys. Sleep Patterns : Going to bed late makes you feel more tired the next morning. Food Preference : Cats prefer fish-flavored food over chicken-flavored food. Shadow Formation : Standing against the light creates longer shadows during the evening. Bubble Size : Using more soap in water creates bigger bubbles. Learning Speed : Kids remember rhymes faster with music than without. Color Attraction : Brightly colored toys attract more attention than dull-colored ones. Simple Hypothesis Statement Examples for Research Paper In academic settings, crafting a straightforward hypothesis helps anchor a research paper, allowing readers to quickly understand the focal point. Here are concise research hypothesis statements ideal for academic exploration. Diet and Cholesterol : A Mediterranean diet lowers cholesterol levels more effectively than a Western diet. Urban Development : Rapid urbanization leads to increased air pollution. Language Acquisition : Immersion learning accelerates second language acquisition compared to classroom learning. Climate Change : Increasing global temperatures directly correlate with rising sea levels. Digital Learning : Online education reduces classroom engagement levels. Consumer Behavior : Discount offers increase sales during festive seasons. Migration Patterns : Economic downturns in a region trigger increased migration. Environmental Conservation : Protected wildlife zones reduce species extinction rates. Cultural Influence : Exposure to global media diminishes local cultural practices. Public Health : Regular public health campaigns reduce the spread of infectious diseases. Simple Hypothesis Statement Examples for Nursing In nursing, hypotheses aim to shed light on patient care, health outcomes, and the intricacies of the medical field. These focused statements offer directions for nursing research and practice. Post-Operative Recovery : Patients with post-operative physiotherapy recover faster than those without. Pain Management : Music therapy reduces the need for pain medication in chronic pain patients. Infant Care : Skin-to-skin contact immediately after birth strengthens mother-infant bonding. Elderly Health : Regular social interactions decrease the onset of dementia in the elderly. Disease Awareness : Regular health check-ups reduce late-stage disease diagnoses. Mental Health : Group therapy sessions enhance coping mechanisms for depression patients. Patient Satisfaction : Nurse-patient ratios are directly proportional to patient satisfaction rates. Medication Adherence : Simplified medication regimens increase adherence in elderly patients. Diet and Recovery : High protein diets speed up wound healing in hospitalized patients. Sleep and Health : Night-shift workers report higher levels of fatigue than day-shift workers. Simple Hypothesis Statement Examples in Psychology Psychology delves deep into the human psyche. Simple pyschology hypothesis in this domain assist in understanding behavioral patterns, cognitive functions, and emotional dynamics. Here are statements reflective of common psychological queries. Child Behavior : Children exposed to violent media exhibit more aggressive behaviors. Memory and Age : Memory recall decreases with advancing age. Group Dynamics : Individuals in larger groups are less likely to exhibit helping behaviors. Stress and Performance : Moderate levels of stress enhance performance on cognitive tasks. Mood and Perception : Positive moods increase the likelihood of optimistic future expectations. Learning Styles : Visual learners retain graphic information better than auditory information. Emotions and Decision Making : Intense emotions impair rational decision-making processes. Peer Pressure : Adolescents are more likely to engage in risk-taking behaviors under peer influence. Personality and Career Choices : Introverts are more drawn to independent job roles than extroverts. Dream Patterns : Exposure to traumatic events increases the frequency of nightmares. Simple Hypothesis Statement Examples for Research Effective research begins with a precise hypothesis. These straightforward declarations guide the investigative journey, providing clarity in discerning outcomes and assessing implications. Technology and Employment : Automation in industries leads to a reduction in manual jobs. Marketing Techniques : Influencer marketing garners more engagement than traditional advertising. Environmental Initiatives : Implementing green technologies reduces corporate carbon footprints. Dietary Habits : Vegan diets result in lower carbon footprints than omnivorous diets. Economic Policies : Tax breaks for the middle class stimulate economic growth. Educational Systems : Student-led learning methods increase long-term knowledge retention. Public Policies : Urban green spaces correlate with reduced crime rates. Cultural Shifts : Increased global travel promotes intercultural understanding and tolerance. Health Trends : Veganism leads to reduced risks of heart-related diseases. Digital Consumption : Increased screen time is linked to declining mental well-being. Simple vs Complex Hypothesis example When it comes to scientific research, hypotheses play a pivotal role in guiding investigations. At the core, a hypothesis is a statement about a potential relationship between variables, or an explanation of an occurrence, which is testable. Based on their structural and conceptual nature, hypotheses can be categorized into simple and complex. A simple hypothesis is a statement that explains the relationship between two variables – one independent variable and one dependent variable. Because it explains the expected relationship in the simplest form, it’s straightforward to test. Example: Drinking caffeine improves short-term memory. In this example: Independent Variable : Caffeine consumption Dependent Variable : Short-term memory Complex Hypothesis A complex good hypothesis , in contrast, deals with the relationship between two or more independent variables and two or more dependent variables. This means it’s multidimensional and requires more intricate testing procedures. Example: Consuming caffeine and sugar together improves short-term memory and reaction time better than consuming either alone. Independent Variables : Caffeine consumption and sugar consumption Dependent Variables : Short-term memory and reaction time How do you write a Simple hypothesis statement? – A Step by Step Guide Step 1: identify your research question. Every hypothesis stems from a research question. For instance, “Does sunlight affect plant growth?” From this, you can create your hypothesis. Step 2: Determine the Variables Every hypothesis has two essential variables: Independent Variable : The cause or reason. (Sunlight in our example) Dependent Variable : The effect or outcome. (Plant growth in our example) Step 3: Formulate the Relationship Specify the expected relationship between the independent and dependent variable. It might be a positive correlation, negative correlation, or no correlation. Step 4: Keep It Testable Ensure your hypothesis can be tested empirically. It should be specific enough that experiments or observations can prove or disprove it. Step 5: Write the Statement Now, construct your statement. For our example, a potential hypothesis might be: Exposing plants to more sunlight will result in faster growth. Tips for Writing Simple Hypothesis Stay Focused : Stick to one specific relationship between an independent and dependent variable. Avoid including multiple causes or effects to keep it simple. Be Clear & Concise : Use clear, straightforward language. Avoid jargon unless it’s necessary for specificity. Ensure It’s Testable : A hypothesis is only useful if you can test it. Avoid abstract or overly broad statements. Base on Existing Research : While your hypothesis will guide new research, it should be grounded in existing theories or observations. Avoid Bias : Ensure your hypothesis doesn’t show any personal beliefs or inclinations. It should be neutral. Revisit & Refine : After writing, revisit your hypothesis. Does it still align with your research question? Is there a clearer way to phrase it? Stay Open to Changes : Based on the results of your tests or experiments, be prepared to change or adjust your hypothesis. Remember, a hypothesis is an educated guess. It’s not a definitive explanation, but rather a proposed one, and its primary purpose is to guide your research. Whether your hypothesis gets supported or refuted, the insights you gain are invaluable. Text prompt Instructive Professional 10 Examples of Public speaking 20 Examples of Gas lighting Pardon Our Interruption As you were browsing something about your browser made us think you were a bot. There are a few reasons this might happen: You've disabled JavaScript in your web browser. You're a power user moving through this website with super-human speed. You've disabled cookies in your web browser. A third-party browser plugin, such as Ghostery or NoScript, is preventing JavaScript from running. Additional information is available in this support article . To regain access, please make sure that cookies and JavaScript are enabled before reloading the page. Scientific Methods What is Hypothesis? We have heard of many hypotheses which have led to great inventions in science. Assumptions that are made on the basis of some evidence are known as hypotheses. In this article, let us learn in detail about the hypothesis and the type of hypothesis with examples. A hypothesis is an assumption that is made based on some evidence. This is the initial point of any investigation that translates the research questions into predictions. It includes components like variables, population and the relation between the variables. A research hypothesis is a hypothesis that is used to test the relationship between two or more variables. Characteristics of Hypothesis Following are the characteristics of the hypothesis: The hypothesis should be clear and precise to consider it to be reliable. If the hypothesis is a relational hypothesis, then it should be stating the relationship between variables. The hypothesis must be specific and should have scope for conducting more tests. The way of explanation of the hypothesis must be very simple and it should also be understood that the simplicity of the hypothesis is not related to its significance. Sources of Hypothesis Following are the sources of hypothesis: The resemblance between the phenomenon. Observations from past studies, present-day experiences and from the competitors. Scientific theories. General patterns that influence the thinking process of people. Types of Hypothesis There are six forms of hypothesis and they are: Simple hypothesis Complex hypothesis Directional hypothesis Non-directional hypothesis Null hypothesis Associative and casual hypothesis Simple Hypothesis It shows a relationship between one dependent variable and a single independent variable. For example – If you eat more vegetables, you will lose weight faster. Here, eating more vegetables is an independent variable, while losing weight is the dependent variable. Complex Hypothesis It shows the relationship between two or more dependent variables and two or more independent variables. Eating more vegetables and fruits leads to weight loss, glowing skin, and reduces the risk of many diseases such as heart disease. Directional Hypothesis It shows how a researcher is intellectual and committed to a particular outcome. The relationship between the variables can also predict its nature. For example- children aged four years eating proper food over a five-year period are having higher IQ levels than children not having a proper meal. This shows the effect and direction of the effect. Non-directional Hypothesis It is used when there is no theory involved. It is a statement that a relationship exists between two variables, without predicting the exact nature (direction) of the relationship. Null Hypothesis It provides a statement which is contrary to the hypothesis. It’s a negative statement, and there is no relationship between independent and dependent variables. The symbol is denoted by “H O ”. Associative and Causal Hypothesis Associative hypothesis occurs when there is a change in one variable resulting in a change in the other variable. Whereas, the causal hypothesis proposes a cause and effect interaction between two or more variables. Examples of Hypothesis Following are the examples of hypotheses based on their types: Consumption of sugary drinks every day leads to obesity is an example of a simple hypothesis. All lilies have the same number of petals is an example of a null hypothesis. If a person gets 7 hours of sleep, then he will feel less fatigue than if he sleeps less. It is an example of a directional hypothesis. Functions of Hypothesis Following are the functions performed by the hypothesis: Hypothesis helps in making an observation and experiments possible. It becomes the start point for the investigation. Hypothesis helps in verifying the observations. It helps in directing the inquiries in the right direction. How will Hypothesis help in the Scientific Method? Researchers use hypotheses to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method: Formation of question Doing background research Creation of hypothesis Designing an experiment Collection of data Result analysis Summarizing the experiment Communicating the results Frequently Asked Questions – FAQs What is hypothesis. A hypothesis is an assumption made based on some evidence. Give an example of simple hypothesis? What are the types of hypothesis. Types of hypothesis are: Associative and Casual hypothesis State true or false: Hypothesis is the initial point of any investigation that translates the research questions into a prediction. Define complex hypothesis.. A complex hypothesis shows the relationship between two or more dependent variables and two or more independent variables. Put your understanding of this concept to test by answering a few MCQs. Click ‘Start Quiz’ to begin! 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A Comprehensive Look at Percentile in Statistics The Best Guide to Understand Bayes Theorem Everything you need to know about the normal distribution, an in-depth explanation of cumulative distribution function, a complete guide to chi-square test, what is hypothesis testing in statistics types and examples, understanding the fundamentals of arithmetic and geometric progression, the definitive guide to understand spearman’s rank correlation, mean squared error: overview, examples, concepts and more, all you need to know about the empirical rule in statistics, the complete guide to skewness and kurtosis, a holistic look at bernoulli distribution. All You Need to Know About Bias in Statistics A Complete Guide to Get a Grasp of Time Series Analysis The Key Differences Between Z-Test Vs. T-Test The Complete Guide to Understand Pearson's Correlation A complete guide on the types of statistical studies, everything you need to know about poisson distribution, your best guide to understand correlation vs. regression, the most comprehensive guide for beginners on what is correlation, hypothesis testing in statistics - types | examples. Lesson 10 of 24 By Avijeet Biswal Table of Contents In today’s data-driven world, decisions are based on data all the time. Hypothesis plays a crucial role in that process, whether it may be making business decisions, in the health sector, academia, or in quality improvement. Without hypothesis & hypothesis tests, you risk drawing the wrong conclusions and making bad decisions. In this tutorial, you will look at Hypothesis Testing in Statistics. The Ultimate Ticket to Top Data Science Job Roles What Is Hypothesis Testing in Statistics? Hypothesis Testing is a type of statistical analysis in which you put your assumptions about a population parameter to the test. It is used to estimate the relationship between 2 statistical variables. Let's discuss few examples of statistical hypothesis from real-life -  A teacher assumes that 60% of his college's students come from lower-middle-class families. A doctor believes that 3D (Diet, Dose, and Discipline) is 90% effective for diabetic patients. Now that you know about hypothesis testing, look at the two types of hypothesis testing in statistics. Hypothesis Testing Formula Z = ( x̅ – μ0 ) / (σ /√n) Here, x̅ is the sample mean, μ0 is the population mean, σ is the standard deviation, n is the sample size. How Hypothesis Testing Works? An analyst performs hypothesis testing on a statistical sample to present evidence of the plausibility of the null hypothesis. Measurements and analyses are conducted on a random sample of the population to test a theory. Analysts use a random population sample to test two hypotheses: the null and alternative hypotheses. The null hypothesis is typically an equality hypothesis between population parameters; for example, a null hypothesis may claim that the population means return equals zero. The alternate hypothesis is essentially the inverse of the null hypothesis (e.g., the population means the return is not equal to zero). As a result, they are mutually exclusive, and only one can be correct. One of the two possibilities, however, will always be correct. Your Dream Career is Just Around The Corner! Null Hypothesis and Alternative Hypothesis The Null Hypothesis is the assumption that the event will not occur. A null hypothesis has no bearing on the study's outcome unless it is rejected. H0 is the symbol for it, and it is pronounced H-naught. The Alternate Hypothesis is the logical opposite of the null hypothesis. The acceptance of the alternative hypothesis follows the rejection of the null hypothesis. H1 is the symbol for it. Let's understand this with an example. A sanitizer manufacturer claims that its product kills 95 percent of germs on average.  To put this company's claim to the test, create a null and alternate hypothesis. H0 (Null Hypothesis): Average = 95%. Alternative Hypothesis (H1): The average is less than 95%. Another straightforward example to understand this concept is determining whether or not a coin is fair and balanced. The null hypothesis states that the probability of a show of heads is equal to the likelihood of a show of tails. In contrast, the alternate theory states that the probability of a show of heads and tails would be very different. Become a Data Scientist with Hands-on Training! Hypothesis Testing Calculation With Examples Let's consider a hypothesis test for the average height of women in the United States. Suppose our null hypothesis is that the average height is 5'4". We gather a sample of 100 women and determine that their average height is 5'5". The standard deviation of population is 2. To calculate the z-score, we would use the following formula: z = ( x̅ – μ0 ) / (σ /√n) z = (5'5" - 5'4") / (2" / √100) z = 0.5 / (0.045) We will reject the null hypothesis as the z-score of 11.11 is very large and conclude that there is evidence to suggest that the average height of women in the US is greater than 5'4". Steps in Hypothesis Testing Hypothesis testing is a statistical method to determine if there is enough evidence in a sample of data to infer that a certain condition is true for the entire population. Here’s a breakdown of the typical steps involved in hypothesis testing: Formulate Hypotheses Null Hypothesis (H0): This hypothesis states that there is no effect or difference, and it is the hypothesis you attempt to reject with your test. Alternative Hypothesis (H1 or Ha): This hypothesis is what you might believe to be true or hope to prove true. It is usually considered the opposite of the null hypothesis. Choose the Significance Level (α) The significance level, often denoted by alpha (α), is the probability of rejecting the null hypothesis when it is true. Common choices for α are 0.05 (5%), 0.01 (1%), and 0.10 (10%). Select the Appropriate Test Choose a statistical test based on the type of data and the hypothesis. Common tests include t-tests, chi-square tests, ANOVA, and regression analysis. The selection depends on data type, distribution, sample size, and whether the hypothesis is one-tailed or two-tailed. Collect Data Gather the data that will be analyzed in the test. This data should be representative of the population to infer conclusions accurately. Calculate the Test Statistic Based on the collected data and the chosen test, calculate a test statistic that reflects how much the observed data deviates from the null hypothesis. Determine the p-value The p-value is the probability of observing test results at least as extreme as the results observed, assuming the null hypothesis is correct. It helps determine the strength of the evidence against the null hypothesis. Make a Decision Compare the p-value to the chosen significance level: If the p-value ≤ α: Reject the null hypothesis, suggesting sufficient evidence in the data supports the alternative hypothesis. If the p-value > α: Do not reject the null hypothesis, suggesting insufficient evidence to support the alternative hypothesis. Report the Results Present the findings from the hypothesis test, including the test statistic, p-value, and the conclusion about the hypotheses. Perform Post-hoc Analysis (if necessary) Depending on the results and the study design, further analysis may be needed to explore the data more deeply or to address multiple comparisons if several hypotheses were tested simultaneously. Types of Hypothesis Testing To determine whether a discovery or relationship is statistically significant, hypothesis testing uses a z-test. It usually checks to see if two means are the same (the null hypothesis). Only when the population standard deviation is known and the sample size is 30 data points or more, can a z-test be applied. A statistical test called a t-test is employed to compare the means of two groups. To determine whether two groups differ or if a procedure or treatment affects the population of interest, it is frequently used in hypothesis testing. Chi-Square  You utilize a Chi-square test for hypothesis testing concerning whether your data is as predicted. To determine if the expected and observed results are well-fitted, the Chi-square test analyzes the differences between categorical variables from a random sample. The test's fundamental premise is that the observed values in your data should be compared to the predicted values that would be present if the null hypothesis were true. Hypothesis Testing and Confidence Intervals Both confidence intervals and hypothesis tests are inferential techniques that depend on approximating the sample distribution. Data from a sample is used to estimate a population parameter using confidence intervals. Data from a sample is used in hypothesis testing to examine a given hypothesis. We must have a postulated parameter to conduct hypothesis testing. Bootstrap distributions and randomization distributions are created using comparable simulation techniques. The observed sample statistic is the focal point of a bootstrap distribution, whereas the null hypothesis value is the focal point of a randomization distribution. A variety of feasible population parameter estimates are included in confidence ranges. In this lesson, we created just two-tailed confidence intervals. There is a direct connection between these two-tail confidence intervals and these two-tail hypothesis tests. The results of a two-tailed hypothesis test and two-tailed confidence intervals typically provide the same results. In other words, a hypothesis test at the 0.05 level will virtually always fail to reject the null hypothesis if the 95% confidence interval contains the predicted value. A hypothesis test at the 0.05 level will nearly certainly reject the null hypothesis if the 95% confidence interval does not include the hypothesized parameter. Become a Data Scientist through hands-on learning with hackathons, masterclasses, webinars, and Ask-Me-Anything! Start learning now! Simple and Composite Hypothesis Testing Depending on the population distribution, you can classify the statistical hypothesis into two types. Simple Hypothesis: A simple hypothesis specifies an exact value for the parameter. Composite Hypothesis: A composite hypothesis specifies a range of values. A company is claiming that their average sales for this quarter are 1000 units. This is an example of a simple hypothesis. Suppose the company claims that the sales are in the range of 900 to 1000 units. Then this is a case of a composite hypothesis. One-Tailed and Two-Tailed Hypothesis Testing The One-Tailed test, also called a directional test, considers a critical region of data that would result in the null hypothesis being rejected if the test sample falls into it, inevitably meaning the acceptance of the alternate hypothesis. In a one-tailed test, the critical distribution area is one-sided, meaning the test sample is either greater or lesser than a specific value. In two tails, the test sample is checked to be greater or less than a range of values in a Two-Tailed test, implying that the critical distribution area is two-sided. If the sample falls within this range, the alternate hypothesis will be accepted, and the null hypothesis will be rejected. Become a Data Scientist With Real-World Experience Right Tailed Hypothesis Testing If the larger than (>) sign appears in your hypothesis statement, you are using a right-tailed test, also known as an upper test. Or, to put it another way, the disparity is to the right. For instance, you can contrast the battery life before and after a change in production. Your hypothesis statements can be the following if you want to know if the battery life is longer than the original (let's say 90 hours): The null hypothesis is (H0 <= 90) or less change. A possibility is that battery life has risen (H1) > 90. The crucial point in this situation is that the alternate hypothesis (H1), not the null hypothesis, decides whether you get a right-tailed test. Left Tailed Hypothesis Testing Alternative hypotheses that assert the true value of a parameter is lower than the null hypothesis are tested with a left-tailed test; they are indicated by the asterisk "<". Suppose H0: mean = 50 and H1: mean not equal to 50 According to the H1, the mean can be greater than or less than 50. This is an example of a Two-tailed test. In a similar manner, if H0: mean >=50, then H1: mean <50 Here the mean is less than 50. It is called a One-tailed test. Type 1 and Type 2 Error A hypothesis test can result in two types of errors. Type 1 Error: A Type-I error occurs when sample results reject the null hypothesis despite being true. Type 2 Error: A Type-II error occurs when the null hypothesis is not rejected when it is false, unlike a Type-I error. Suppose a teacher evaluates the examination paper to decide whether a student passes or fails. H0: Student has passed H1: Student has failed Type I error will be the teacher failing the student [rejects H0] although the student scored the passing marks [H0 was true].  Type II error will be the case where the teacher passes the student [do not reject H0] although the student did not score the passing marks [H1 is true]. Our Data Scientist Master's Program covers core topics such as R, Python, Machine Learning, Tableau, Hadoop, and Spark. Get started on your journey today! Limitations of Hypothesis Testing Hypothesis testing has some limitations that researchers should be aware of: It cannot prove or establish the truth: Hypothesis testing provides evidence to support or reject a hypothesis, but it cannot confirm the absolute truth of the research question. Results are sample-specific: Hypothesis testing is based on analyzing a sample from a population, and the conclusions drawn are specific to that particular sample. Possible errors: During hypothesis testing, there is a chance of committing type I error (rejecting a true null hypothesis) or type II error (failing to reject a false null hypothesis). Assumptions and requirements: Different tests have specific assumptions and requirements that must be met to accurately interpret results. Learn All The Tricks Of The BI Trade After reading this tutorial, you would have a much better understanding of hypothesis testing, one of the most important concepts in the field of Data Science . The majority of hypotheses are based on speculation about observed behavior, natural phenomena, or established theories. If you are interested in statistics of data science and skills needed for such a career, you ought to explore the Post Graduate Program in Data Science. If you have any questions regarding this ‘Hypothesis Testing In Statistics’ tutorial, do share them in the comment section. Our subject matter expert will respond to your queries. Happy learning! 1. What is hypothesis testing in statistics with example? Hypothesis testing is a statistical method used to determine if there is enough evidence in a sample data to draw conclusions about a population. It involves formulating two competing hypotheses, the null hypothesis (H0) and the alternative hypothesis (Ha), and then collecting data to assess the evidence. An example: testing if a new drug improves patient recovery (Ha) compared to the standard treatment (H0) based on collected patient data. 2. What is H0 and H1 in statistics? In statistics, H0​ and H1​ represent the null and alternative hypotheses. The null hypothesis, H0​, is the default assumption that no effect or difference exists between groups or conditions. The alternative hypothesis, H1​, is the competing claim suggesting an effect or a difference. Statistical tests determine whether to reject the null hypothesis in favor of the alternative hypothesis based on the data. 3. What is a simple hypothesis with an example? A simple hypothesis is a specific statement predicting a single relationship between two variables. It posits a direct and uncomplicated outcome. For example, a simple hypothesis might state, "Increased sunlight exposure increases the growth rate of sunflowers." Here, the hypothesis suggests a direct relationship between the amount of sunlight (independent variable) and the growth rate of sunflowers (dependent variable), with no additional variables considered. 4. What are the 3 major types of hypothesis? The three major types of hypotheses are: Null Hypothesis (H0): Represents the default assumption, stating that there is no significant effect or relationship in the data. Alternative Hypothesis (Ha): Contradicts the null hypothesis and proposes a specific effect or relationship that researchers want to investigate. Nondirectional Hypothesis: An alternative hypothesis that doesn't specify the direction of the effect, leaving it open for both positive and negative possibilities. Find our PL-300 Microsoft Power BI Certification Training Online Classroom training classes in top cities: Name Date Place 21 Sep -6 Oct 2024, Weekend batch Your City 12 Oct -27 Oct 2024, Weekend batch Chicago 25 Oct -9 Nov 2024, Weekdays batch Houston About the Author Avijeet is a Senior Research Analyst at Simplilearn. Passionate about Data Analytics, Machine Learning, and Deep Learning, Avijeet is also interested in politics, cricket, and football. Recommended Resources Free eBook: Top Programming Languages For A Data Scientist Normality Test in Minitab: Minitab with Statistics Machine Learning Career Guide: A Playbook to Becoming a Machine Learning Engineer PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc. 412 IMAGES How to Write a Hypothesis: The Ultimate Guide with Examples 13 Different Types of Hypothesis (2024) Research Hypothesis: Definition, Types, Examples and Quick Tips Examples of Hypothesis: 15+ Ideas to Help You Formulate Yours What is an Hypothesis Hypothesis and its types VIDEO Two-Sample Hypothesis Testing: Dependent Sample Fun Example Hypothesis Testing for Two Populations Hypothesis Meaning in Bengali Hypothesis Testing Hypothesis Testing Statistics Unit # 7 Understanding Hypothesis Testing: Definition and 4 Steps for Testing with Example COMMENTS How to Write a Hypothesis in 6 Steps, With Examples Examples: Customers at restaurants will tip the same even if the wait staff's base salary is raised. Washing your hands every hour can reduce the frequency of illness. 7 Statistical hypothesis. A statistical hypothesis is when you test only a sample of a population and then apply statistical evidence to the results to draw a conclusion about ... 15 Hypothesis Examples (2024) The simple hypothesis is a prediction about the relationship between two variables, excluding any other variables from consideration. This example posits that by reducing time spent on devices like smartphones and computers, an individual should experience improved sleep quality. How to Write a Strong Hypothesis Developing a hypothesis (with example) Step 1. Ask a question. Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project. Example: Research question. Hypothesis: Definition, Examples, and Types A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process. Consider a study designed to examine the relationship between sleep deprivation and test ... Hypothesis Examples Research Hypothesis Examples. A research hypothesis (H 1) is a type of hypothesis used to design an experiment. This type of hypothesis is often written as an if-then statement because it's easy identifying the independent and dependent variables and seeing how one affects the other. If-then statements explore cause and effect. What is a Research Hypothesis: How to Write it, Types, and Examples Simple hypothesis: A simple hypothesis only predicts the relationship between one independent and another independent variable. Example: " Applying sunscreen every day slows skin aging." 6. Complex hypothesis: A complex hypothesis states the relationship or difference between two or more independent and dependent variables. Research Hypothesis: Definition, Types, Examples and Quick Tips Simple hypothesis. A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, "Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking. 4. How to Write a Hypothesis w/ Strong Examples Hypothesis Examples Simple Hypothesis Examples. Increasing the amount of natural light in a classroom will improve students' test scores. Drinking at least eight glasses of water a day reduces the frequency of headaches in adults. Plant growth is faster when the plant is exposed to music for at least one hour per day. Complex Hypothesis Examples What is a Hypothesis A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor. ... For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research. Characteristics of Hypothesis ... How to Write a Strong Hypothesis Step 5: Phrase your hypothesis in three ways. To identify the variables, you can write a simple prediction in if … then form. The first part of the sentence states the independent variable and the second part states the dependent variable. If a first-year student starts attending more lectures, then their exam scores will improve. Research Hypothesis In Psychology: Types, & Examples Examples. A research hypothesis, in its plural form "hypotheses," is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding. How to Write a Research Hypothesis: Good & Bad Examples Another example for a directional one-tailed alternative hypothesis would be that. H1: Attending private classes before important exams has a positive effect on performance. Your null hypothesis would then be that. H0: Attending private classes before important exams has no/a negative effect on performance. What Are Effective Hypothesis Examples? Hypotheses Examples: If, Then. If you get at least 6 hours of sleep, you will do better on tests than if you get less sleep. If you drop a ball, it will fall toward the ground. If you drink coffee before going to bed, then it will take longer to fall asleep. If you cover a wound with a bandage, then it will heal with less scarring. What Is A Research Hypothesis? A Simple Definition A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes - specificity, clarity and testability. Let's take a look at these more closely. 36 Examples of a Hypothesis The following are illustrative examples of a hypothesis. Plants will grow faster in blue light as compared to red or green light.Regular watering can desalinate soil in a pot.Local air quality is better on weekends and holidays.Tennis balls bounce higher when they are cold.There is significant variation in the average amount of pollen in ... 13 Different Types of Hypothesis (2024) In the above example, we have multiple independent and dependent variables: Independent variables: Age and weight. Dependent variables: diabetes and heart disease. Because there are multiple variables, this study is a lot more complex than a simple hypothesis.It quickly gets much more difficult to prove these hypotheses. Hypothesis Testing Present the findings in your results and discussion section. Though the specific details might vary, the procedure you will use when testing a hypothesis will always follow some version of these steps. Table of contents. Step 1: State your null and alternate hypothesis. Step 2: Collect data. Step 3: Perform a statistical test. How to Write a Hypothesis: 5 Simple Steps & Examples Step 1. Generate a Question Before Writing Your Hypothesis. At the first stage of writing a hypothesis for a research paper you must define a research question that you need to answer. It should be focused on particular problem. Try to make it specific and yet suitable for research within framework of your project. Hypothesis Examples: Different Types in Science and Research To form a solid theory, the vital first step is creating a hypothesis. See the various types of hypotheses and how they can lead you on the path to discovery. Simple Hypothesis A simple hypothesis statement typically specifies a relationship or difference between two variables. Here's an example: Caffeine Consumption and Alertness: Consuming caffeine increases a person's alertness. In this example, the two variables are "caffeine consumption" and "alertness.". The hypothesis simply posits that there's a ... Crafting Effective Hypothesis Statements: Examples & Best Hypothesis Statements - Overview and Template This document contains definitions, examples, and a template to complete for your assignment. Hypothesis Statements Overview A hypothesis is a prediction about the relationship between two variables. Hypotheses statements often start as an educated guess about how one variable affects a second variable. A hypothesis statement must be testable (i.e ... What is Hypothesis Following are the examples of hypotheses based on their types: Consumption of sugary drinks every day leads to obesity is an example of a simple hypothesis. All lilies have the same number of petals is an example of a null hypothesis. If a person gets 7 hours of sleep, then he will feel less fatigue than if he sleeps less. Hypothesis Testing in Statistics What is a simple hypothesis with an example? A simple hypothesis is a specific statement predicting a single relationship between two variables. It posits a direct and uncomplicated outcome. For example, a simple hypothesis might state, "Increased sunlight exposure increases the growth rate of sunflowers." Here, the hypothesis suggests a direct ... Simple hypothesis testing Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/probability/probability-and-combinatorics-topic/decisions-with-p... essay homework paper phd project resume review study thesis