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Experiment Definition in Science – What Is a Science Experiment?

In science, an experiment is simply a test of a hypothesis in the scientific method . It is a controlled examination of cause and effect. Here is a look at what a science experiment is (and is not), the key factors in an experiment, examples, and types of experiments.

Experiment Definition in Science

By definition, an experiment is a procedure that tests a hypothesis. A hypothesis, in turn, is a prediction of cause and effect or the predicted outcome of changing one factor of a situation. Both the hypothesis and experiment are components of the scientific method. The steps of the scientific method are:

• Make observations.
• Ask a question or identify a problem.
• State a hypothesis.
• Perform an experiment that tests the hypothesis.
• Based on the results of the experiment, either accept or reject the hypothesis.
• Draw conclusions and report the outcome of the experiment.

Key Parts of an Experiment

The two key parts of an experiment are the independent and dependent variables. The independent variable is the one factor that you control or change in an experiment. The dependent variable is the factor that you measure that responds to the independent variable. An experiment often includes other types of variables , but at its heart, it’s all about the relationship between the independent and dependent variable.

Examples of Experiments

Fertilizer and plant size.

For example, you think a certain fertilizer helps plants grow better. You’ve watched your plants grow and they seem to do better when they have the fertilizer compared to when they don’t. But, observations are only the beginning of science. So, you state a hypothesis: Adding fertilizer increases plant size. Note, you could have stated the hypothesis in different ways. Maybe you think the fertilizer increases plant mass or fruit production, for example. However you state the hypothesis, it includes both the independent and dependent variables. In this case, the independent variable is the presence or absence of fertilizer. The dependent variable is the response to the independent variable, which is the size of the plants.

Now that you have a hypothesis, the next step is designing an experiment that tests it. Experimental design is very important because the way you conduct an experiment influences its outcome. For example, if you use too small of an amount of fertilizer you may see no effect from the treatment. Or, if you dump an entire container of fertilizer on a plant you could kill it! So, recording the steps of the experiment help you judge the outcome of the experiment and aid others who come after you and examine your work. Other factors that might influence your results might include the species of plant and duration of the treatment. Record any conditions that might affect the outcome. Ideally, you want the only difference between your two groups of plants to be whether or not they receive fertilizer. Then, measure the height of the plants and see if there is a difference between the two groups.

Salt and Cookies

You don’t need a lab for an experiment. For example, consider a baking experiment. Let’s say you like the flavor of salt in your cookies, but you’re pretty sure the batch you made using extra salt fell a bit flat. If you double the amount of salt in a recipe, will it affect their size? Here, the independent variable is the amount of salt in the recipe and the dependent variable is cookie size.

Test this hypothesis with an experiment. Bake cookies using the normal recipe (your control group ) and bake some using twice the salt (the experimental group). Make sure it’s the exact same recipe. Bake the cookies at the same temperature and for the same time. Only change the amount of salt in the recipe. Then measure the height or diameter of the cookies and decide whether to accept or reject the hypothesis.

Examples of Things That Are Not Experiments

Based on the examples of experiments, you should see what is not an experiment:

• Making observations does not constitute an experiment. Initial observations often lead to an experiment, but are not a substitute for one.
• Making a model is not an experiment.
• Neither is making a poster.
• Just trying something to see what happens is not an experiment. You need a hypothesis or prediction about the outcome.
• Changing a lot of things at once isn’t an experiment. You only have one independent and one dependent variable. However, in an experiment, you might suspect the independent variable has an effect on a separate. So, you design a new experiment to test this.

Types of Experiments

There are three main types of experiments: controlled experiments, natural experiments, and field experiments,

• Controlled experiment : A controlled experiment compares two groups of samples that differ only in independent variable. For example, a drug trial compares the effect of a group taking a placebo (control group) against those getting the drug (the treatment group). Experiments in a lab or home generally are controlled experiments
• Natural experiment : Another name for a natural experiment is a quasi-experiment. In this type of experiment, the researcher does not directly control the independent variable, plus there may be other variables at play. Here, the goal is establishing a correlation between the independent and dependent variable. For example, in the formation of new elements a scientist hypothesizes that a certain collision between particles creates a new atom. But, other outcomes may be possible. Or, perhaps only decay products are observed that indicate the element, and not the new atom itself. Many fields of science rely on natural experiments, since controlled experiments aren’t always possible.
• Field experiment : While a controlled experiments takes place in a lab or other controlled setting, a field experiment occurs in a natural setting. Some phenomena cannot be readily studied in a lab or else the setting exerts an influence that affects the results. So, a field experiment may have higher validity. However, since the setting is not controlled, it is also subject to external factors and potential contamination. For example, if you study whether a certain plumage color affects bird mate selection, a field experiment in a natural environment eliminates the stressors of an artificial environment. Yet, other factors that could be controlled in a lab may influence results. For example, nutrition and health are controlled in a lab, but not in the field.
• Bailey, R.A. (2008). Design of Comparative Experiments . Cambridge: Cambridge University Press. ISBN 9780521683579.
• di Francia, G. Toraldo (1981). The Investigation of the Physical World . Cambridge University Press. ISBN 0-521-29925-X.
• Hinkelmann, Klaus; Kempthorne, Oscar (2008). Design and Analysis of Experiments. Volume I: Introduction to Experimental Design (2nd ed.). Wiley. ISBN 978-0-471-72756-9.
• Holland, Paul W. (December 1986). “Statistics and Causal Inference”.  Journal of the American Statistical Association . 81 (396): 945–960. doi: 10.2307/2289064
• Stohr-Hunt, Patricia (1996). “An Analysis of Frequency of Hands-on Experience and Science Achievement”. Journal of Research in Science Teaching . 33 (1): 101–109. doi: 10.1002/(SICI)1098-2736(199601)33:1<101::AID-TEA6>3.0.CO;2-Z

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Definition of experiment

 (Entry 1 of 2)

Definition of experiment  (Entry 2 of 2)

intransitive verb

• experimentation

Examples of experiment in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'experiment.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

Middle English, "testing, proof, remedy," borrowed from Anglo-French esperiment, borrowed from Latin experīmentum "testing, experience, proof," from experīrī "to put to the test, attempt, have experience of, undergo" + -mentum -ment — more at experience entry 1

verbal derivative of experiment entry 1

14th century, in the meaning defined at sense 1a

1787, in the meaning defined above

Phrases Containing experiment

• control experiment
• controlled experiment
• experiment station
• pre - experiment
• thought experiment

Articles Related to experiment

This is the Difference Between a...

This is the Difference Between a Hypothesis and a Theory

In scientific reasoning, they're two completely different things

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experimental

Cite this Entry

“Experiment.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/experiment. Accessed 7 Sep. 2024.

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Kids Definition of experiment  (Entry 2 of 2)

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Medical Definition of experiment  (Entry 2 of 2)

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Experimentation in Scientific Research: Variables and controls in practice

by Anthony Carpi, Ph.D., Anne E. Egger, Ph.D.

Listen to this reading

Did you know that experimental design was developed more than a thousand years ago by a Middle Eastern scientist who studied light? All of us use a form of experimental research in our day to day lives when we try to find the spot with the best cell phone reception, try out new cooking recipes, and more. Scientific experiments are built on similar principles.

Experimentation is a research method in which one or more variables are consciously manipulated and the outcome or effect of that manipulation on other variables is observed.

Experimental designs often make use of controls that provide a measure of variability within a system and a check for sources of error.

Experimental methods are commonly applied to determine causal relationships or to quantify the magnitude of response of a variable.

Anyone who has used a cellular phone knows that certain situations require a bit of research: If you suddenly find yourself in an area with poor phone reception, you might move a bit to the left or right, walk a few steps forward or back, or even hold the phone over your head to get a better signal. While the actions of a cell phone user might seem obvious, the person seeking cell phone reception is actually performing a scientific experiment: consciously manipulating one component (the location of the cell phone) and observing the effect of that action on another component (the phone's reception). Scientific experiments are obviously a bit more complicated, and generally involve more rigorous use of controls , but they draw on the same type of reasoning that we use in many everyday situations. In fact, the earliest documented scientific experiments were devised to answer a very common everyday question: how vision works.

• A brief history of experimental methods

Figure 1: Alhazen (965-ca.1039) as pictured on an Iraqi 10,000-dinar note

One of the first ideas regarding how human vision works came from the Greek philosopher Empedocles around 450 BCE . Empedocles reasoned that the Greek goddess Aphrodite had lit a fire in the human eye, and vision was possible because light rays from this fire emanated from the eye, illuminating objects around us. While a number of people challenged this proposal, the idea that light radiated from the human eye proved surprisingly persistent until around 1,000 CE , when a Middle Eastern scientist advanced our knowledge of the nature of light and, in so doing, developed a new and more rigorous approach to scientific research . Abū 'Alī al-Hasan ibn al-Hasan ibn al-Haytham, also known as Alhazen , was born in 965 CE in the Arabian city of Basra in what is present-day Iraq. He began his scientific studies in physics, mathematics, and other sciences after reading the works of several Greek philosophers. One of Alhazen's most significant contributions was a seven-volume work on optics titled Kitab al-Manazir (later translated to Latin as Opticae Thesaurus Alhazeni – Alhazen's Book of Optics ). Beyond the contributions this book made to the field of optics, it was a remarkable work in that it based conclusions on experimental evidence rather than abstract reasoning – the first major publication to do so. Alhazen's contributions have proved so significant that his likeness was immortalized on the 2003 10,000-dinar note issued by Iraq (Figure 1).

Alhazen invested significant time studying light , color, shadows, rainbows, and other optical phenomena. Among this work was a study in which he stood in a darkened room with a small hole in one wall. Outside of the room, he hung two lanterns at different heights. Alhazen observed that the light from each lantern illuminated a different spot in the room, and each lighted spot formed a direct line with the hole and one of the lanterns outside the room. He also found that covering a lantern caused the spot it illuminated to darken, and exposing the lantern caused the spot to reappear. Thus, Alhazen provided some of the first experimental evidence that light does not emanate from the human eye but rather is emitted by certain objects (like lanterns) and travels from these objects in straight lines. Alhazen's experiment may seem simplistic today, but his methodology was groundbreaking: He developed a hypothesis based on observations of physical relationships (that light comes from objects), and then designed an experiment to test that hypothesis. Despite the simplicity of the method , Alhazen's experiment was a critical step in refuting the long-standing theory that light emanated from the human eye, and it was a major event in the development of modern scientific research methodology.

Comprehension Checkpoint

• Experimentation as a scientific research method

Experimentation is one scientific research method , perhaps the most recognizable, in a spectrum of methods that also includes description, comparison, and modeling (see our Description , Comparison , and Modeling modules). While all of these methods share in common a scientific approach, experimentation is unique in that it involves the conscious manipulation of certain aspects of a real system and the observation of the effects of that manipulation. You could solve a cell phone reception problem by walking around a neighborhood until you see a cell phone tower, observing other cell phone users to see where those people who get the best reception are standing, or looking on the web for a map of cell phone signal coverage. All of these methods could also provide answers, but by moving around and testing reception yourself, you are experimenting.

• Variables: Independent and dependent

In the experimental method , a condition or a parameter , generally referred to as a variable , is consciously manipulated (often referred to as a treatment) and the outcome or effect of that manipulation is observed on other variables. Variables are given different names depending on whether they are the ones manipulated or the ones observed:

• Independent variable refers to a condition within an experiment that is manipulated by the scientist.
• Dependent variable refers to an event or outcome of an experiment that might be affected by the manipulation of the independent variable .

Scientific experimentation helps to determine the nature of the relationship between independent and dependent variables . While it is often difficult, or sometimes impossible, to manipulate a single variable in an experiment , scientists often work to minimize the number of variables being manipulated. For example, as we move from one location to another to get better cell reception, we likely change the orientation of our body, perhaps from south-facing to east-facing, or we hold the cell phone at a different angle. Which variable affected reception: location, orientation, or angle of the phone? It is critical that scientists understand which aspects of their experiment they are manipulating so that they can accurately determine the impacts of that manipulation . In order to constrain the possible outcomes of an experimental procedure, most scientific experiments use a system of controls .

• Controls: Negative, positive, and placebos

In a controlled study, a scientist essentially runs two (or more) parallel and simultaneous experiments: a treatment group, in which the effect of an experimental manipulation is observed on a dependent variable , and a control group, which uses all of the same conditions as the first with the exception of the actual treatment. Controls can fall into one of two groups: negative controls and positive controls .

In a negative control , the control group is exposed to all of the experimental conditions except for the actual treatment . The need to match all experimental conditions exactly is so great that, for example, in a trial for a new drug, the negative control group will be given a pill or liquid that looks exactly like the drug, except that it will not contain the drug itself, a control often referred to as a placebo . Negative controls allow scientists to measure the natural variability of the dependent variable(s), provide a means of measuring error in the experiment , and also provide a baseline to measure against the experimental treatment.

Some experimental designs also make use of positive controls . A positive control is run as a parallel experiment and generally involves the use of an alternative treatment that the researcher knows will have an effect on the dependent variable . For example, when testing the effectiveness of a new drug for pain relief, a scientist might administer treatment placebo to one group of patients as a negative control , and a known treatment like aspirin to a separate group of individuals as a positive control since the pain-relieving aspects of aspirin are well documented. In both cases, the controls allow scientists to quantify background variability and reject alternative hypotheses that might otherwise explain the effect of the treatment on the dependent variable .

• Experimentation in practice: The case of Louis Pasteur

Well-controlled experiments generally provide strong evidence of causality, demonstrating whether the manipulation of one variable causes a response in another variable. For example, as early as the 6th century BCE , Anaximander , a Greek philosopher, speculated that life could be formed from a mixture of sea water, mud, and sunlight. The idea probably stemmed from the observation of worms, mosquitoes, and other insects "magically" appearing in mudflats and other shallow areas. While the suggestion was challenged on a number of occasions, the idea that living microorganisms could be spontaneously generated from air persisted until the middle of the 18 th century.

In the 1750s, John Needham, a Scottish clergyman and naturalist, claimed to have proved that spontaneous generation does occur when he showed that microorganisms flourished in certain foods such as soup broth, even after they had been briefly boiled and covered. Several years later, the Italian abbot and biologist Lazzaro Spallanzani , boiled soup broth for over an hour and then placed bowls of this soup in different conditions, sealing some and leaving others exposed to air. Spallanzani found that microorganisms grew in the soup exposed to air but were absent from the sealed soup. He therefore challenged Needham's conclusions and hypothesized that microorganisms suspended in air settled onto the exposed soup but not the sealed soup, and rejected the idea of spontaneous generation .

Needham countered, arguing that the growth of bacteria in the soup was not due to microbes settling onto the soup from the air, but rather because spontaneous generation required contact with an intangible "life force" in the air itself. He proposed that Spallanzani's extensive boiling destroyed the "life force" present in the soup, preventing spontaneous generation in the sealed bowls but allowing air to replenish the life force in the open bowls. For several decades, scientists continued to debate the spontaneous generation theory of life, with support for the theory coming from several notable scientists including Félix Pouchet and Henry Bastion. Pouchet, Director of the Rouen Museum of Natural History in France, and Bastion, a well-known British bacteriologist, argued that living organisms could spontaneously arise from chemical processes such as fermentation and putrefaction. The debate became so heated that in 1860, the French Academy of Sciences established the Alhumbert prize of 2,500 francs to the first person who could conclusively resolve the conflict. In 1864, Louis Pasteur achieved that result with a series of well-controlled experiments and in doing so claimed the Alhumbert prize.

Pasteur prepared for his experiments by studying the work of others that came before him. In fact, in April 1861 Pasteur wrote to Pouchet to obtain a research description that Pouchet had published. In this letter, Pasteur writes:

Paris, April 3, 1861 Dear Colleague, The difference of our opinions on the famous question of spontaneous generation does not prevent me from esteeming highly your labor and praiseworthy efforts... The sincerity of these sentiments...permits me to have recourse to your obligingness in full confidence. I read with great care everything that you write on the subject that occupies both of us. Now, I cannot obtain a brochure that I understand you have just published.... I would be happy to have a copy of it because I am at present editing the totality of my observations, where naturally I criticize your assertions. L. Pasteur (Porter, 1961)

Pasteur received the brochure from Pouchet several days later and went on to conduct his own experiments . In these, he repeated Spallanzani's method of boiling soup broth, but he divided the broth into portions and exposed these portions to different controlled conditions. Some broth was placed in flasks that had straight necks that were open to the air, some broth was placed in sealed flasks that were not open to the air, and some broth was placed into a specially designed set of swan-necked flasks, in which the broth would be open to the air but the air would have to travel a curved path before reaching the broth, thus preventing anything that might be present in the air from simply settling onto the soup (Figure 2). Pasteur then observed the response of the dependent variable (the growth of microorganisms) in response to the independent variable (the design of the flask). Pasteur's experiments contained both positive controls (samples in the straight-necked flasks that he knew would become contaminated with microorganisms) and negative controls (samples in the sealed flasks that he knew would remain sterile). If spontaneous generation did indeed occur upon exposure to air, Pasteur hypothesized, microorganisms would be found in both the swan-neck flasks and the straight-necked flasks, but not in the sealed flasks. Instead, Pasteur found that microorganisms appeared in the straight-necked flasks, but not in the sealed flasks or the swan-necked flasks.

Figure 2: Pasteur's drawings of the flasks he used (Pasteur, 1861). Fig. 25 D, C, and B (top) show various sealed flasks (negative controls); Fig. 26 (bottom right) illustrates a straight-necked flask directly open to the atmosphere (positive control); and Fig. 25 A (bottom left) illustrates the specially designed swan-necked flask (treatment group).

By using controls and replicating his experiment (he used more than one of each type of flask), Pasteur was able to answer many of the questions that still surrounded the issue of spontaneous generation. Pasteur said of his experimental design, "I affirm with the most perfect sincerity that I have never had a single experiment, arranged as I have just explained, which gave me a doubtful result" (Porter, 1961). Pasteur's work helped refute the theory of spontaneous generation – his experiments showed that air alone was not the cause of bacterial growth in the flask, and his research supported the hypothesis that live microorganisms suspended in air could settle onto the broth in open-necked flasks via gravity .

• Experimentation across disciplines

Experiments are used across all scientific disciplines to investigate a multitude of questions. In some cases, scientific experiments are used for exploratory purposes in which the scientist does not know what the dependent variable is. In this type of experiment, the scientist will manipulate an independent variable and observe what the effect of the manipulation is in order to identify a dependent variable (or variables). Exploratory experiments are sometimes used in nutritional biology when scientists probe the function and purpose of dietary nutrients . In one approach, a scientist will expose one group of animals to a normal diet, and a second group to a similar diet except that it is lacking a specific vitamin or nutrient. The researcher will then observe the two groups to see what specific physiological changes or medical problems arise in the group lacking the nutrient being studied.

Scientific experiments are also commonly used to quantify the magnitude of a relationship between two or more variables . For example, in the fields of pharmacology and toxicology, scientific experiments are used to determine the dose-response relationship of a new drug or chemical. In these approaches, researchers perform a series of experiments in which a population of organisms , such as laboratory mice, is separated into groups and each group is exposed to a different amount of the drug or chemical of interest. The analysis of the data that result from these experiments (see our Data Analysis and Interpretation module) involves comparing the degree of the organism's response to the dose of the substance administered.

In this context, experiments can provide additional evidence to complement other research methods . For example, in the 1950s a great debate ensued over whether or not the chemicals in cigarette smoke cause cancer. Several researchers had conducted comparative studies (see our Comparison in Scientific Research module) that indicated that patients who smoked had a higher probability of developing lung cancer when compared to nonsmokers. Comparative studies differ slightly from experimental methods in that you do not consciously manipulate a variable ; rather you observe differences between two or more groups depending on whether or not they fall into a treatment or control group. Cigarette companies and lobbyists criticized these studies, suggesting that the relationship between smoking and lung cancer was coincidental. Several researchers noted the need for a clear dose-response study; however, the difficulties in getting cigarette smoke into the lungs of laboratory animals prevented this research. In the mid-1950s, Ernest Wynder and colleagues had an ingenious idea: They condensed the chemicals from cigarette smoke into a liquid and applied this in various doses to the skin of groups of mice. The researchers published data from a dose-response experiment of the effect of tobacco smoke condensate on mice (Wynder et al., 1957).

As seen in Figure 3, the researchers found a positive relationship between the amount of condensate applied to the skin of mice and the number of cancers that developed. The graph shows the results of a study in which different groups of mice were exposed to increasing amounts of cigarette tar. The black dots indicate the percentage of each sample group of mice that developed cancer for a given amount cigarette smoke "condensate" applied to their skin. The vertical lines are error bars, showing the amount of uncertainty . The graph shows generally increasing cancer rates with greater exposure. This study was one of the first pieces of experimental evidence in the cigarette smoking debate , and it helped strengthen the case for cigarette smoke as the causative agent in lung cancer in smokers.

Figure 3: Percentage of mice with cancer versus the amount cigarette smoke "condensate" applied to their skin (source: Wynder et al., 1957).

Sometimes experimental approaches and other research methods are not clearly distinct, or scientists may even use multiple research approaches in combination. For example, at 1:52 a.m. EDT on July 4, 2005, scientists with the National Aeronautics and Space Administration (NASA) conducted a study in which a 370 kg spacecraft named Deep Impact was purposely slammed into passing comet Tempel 1. A nearby spacecraft observed the impact and radioed data back to Earth. The research was partially descriptive in that it documented the chemical composition of the comet, but it was also partly experimental in that the effect of slamming the Deep Impact probe into the comet on the volatilization of previously undetected compounds , such as water, was assessed (A'Hearn et al., 2005). It is particularly common that experimentation and description overlap: Another example is Jane Goodall 's research on the behavior of chimpanzees, which can be read in our Description in Scientific Research module.

• Limitations of experimental methods

Figure 4: An image of comet Tempel 1 67 seconds after collision with the Deep Impact impactor. Image credit: NASA/JPL-Caltech/UMD http://deepimpact.umd.edu/gallery/HRI_937_1.html

While scientific experiments provide invaluable data regarding causal relationships, they do have limitations. One criticism of experiments is that they do not necessarily represent real-world situations. In order to clearly identify the relationship between an independent variable and a dependent variable , experiments are designed so that many other contributing variables are fixed or eliminated. For example, in an experiment designed to quantify the effect of vitamin A dose on the metabolism of beta-carotene in humans, Shawna Lemke and colleagues had to precisely control the diet of their human volunteers (Lemke, Dueker et al. 2003). They asked their participants to limit their intake of foods rich in vitamin A and further asked that they maintain a precise log of all foods eaten for 1 week prior to their study. At the time of their study, they controlled their participants' diet by feeding them all the same meals, described in the methods section of their research article in this way:

Meals were controlled for time and content on the dose administration day. Lunch was served at 5.5 h postdosing and consisted of a frozen dinner (Enchiladas, Amy's Kitchen, Petaluma, CA), a blueberry bagel with jelly, 1 apple and 1 banana, and a large chocolate chunk cookie (Pepperidge Farm). Dinner was served 10.5 h post dose and consisted of a frozen dinner (Chinese Stir Fry, Amy's Kitchen) plus the bagel and fruit taken for lunch.

While this is an important aspect of making an experiment manageable and informative, it is often not representative of the real world, in which many variables may change at once, including the foods you eat. Still, experimental research is an excellent way of determining relationships between variables that can be later validated in real world settings through descriptive or comparative studies.

Design is critical to the success or failure of an experiment . Slight variations in the experimental set-up could strongly affect the outcome being measured. For example, during the 1950s, a number of experiments were conducted to evaluate the toxicity in mammals of the metal molybdenum, using rats as experimental subjects . Unexpectedly, these experiments seemed to indicate that the type of cage the rats were housed in affected the toxicity of molybdenum. In response, G. Brinkman and Russell Miller set up an experiment to investigate this observation (Brinkman & Miller, 1961). Brinkman and Miller fed two groups of rats a normal diet that was supplemented with 200 parts per million (ppm) of molybdenum. One group of rats was housed in galvanized steel (steel coated with zinc to reduce corrosion) cages and the second group was housed in stainless steel cages. Rats housed in the galvanized steel cages suffered more from molybdenum toxicity than the other group: They had higher concentrations of molybdenum in their livers and lower blood hemoglobin levels. It was then shown that when the rats chewed on their cages, those housed in the galvanized metal cages absorbed zinc plated onto the metal bars, and zinc is now known to affect the toxicity of molybdenum. In order to control for zinc exposure, then, stainless steel cages needed to be used for all rats.

Scientists also have an obligation to adhere to ethical limits in designing and conducting experiments . During World War II, doctors working in Nazi Germany conducted many heinous experiments using human subjects . Among them was an experiment meant to identify effective treatments for hypothermia in humans, in which concentration camp prisoners were forced to sit in ice water or left naked outdoors in freezing temperatures and then re-warmed by various means. Many of the exposed victims froze to death or suffered permanent injuries. As a result of the Nazi experiments and other unethical research , strict scientific ethical standards have been adopted by the United States and other governments, and by the scientific community at large. Among other things, ethical standards (see our Scientific Ethics module) require that the benefits of research outweigh the risks to human subjects, and those who participate do so voluntarily and only after they have been made fully aware of all the risks posed by the research. These guidelines have far-reaching effects: While the clearest indication of causation in the cigarette smoke and lung cancer debate would have been to design an experiment in which one group of people was asked to take up smoking and another group was asked to refrain from smoking, it would be highly unethical for a scientist to purposefully expose a group of healthy people to a suspected cancer causing agent. As an alternative, comparative studies (see our Comparison in Scientific Research module) were initiated in humans, and experimental studies focused on animal subjects. The combination of these and other studies provided even stronger evidence of the link between smoking and lung cancer than either one method alone would have.

• Experimentation in modern practice

Like all scientific research , the results of experiments are shared with the scientific community, are built upon, and inspire additional experiments and research. For example, once Alhazen established that light given off by objects enters the human eye, the natural question that was asked was "What is the nature of light that enters the human eye?" Two common theories about the nature of light were debated for many years. Sir Isaac Newton was among the principal proponents of a theory suggesting that light was made of small particles . The English naturalist Robert Hooke (who held the interesting title of Curator of Experiments at the Royal Society of London) supported a different theory stating that light was a type of wave, like sound waves . In 1801, Thomas Young conducted a now classic scientific experiment that helped resolve this controversy . Young, like Alhazen, worked in a darkened room and allowed light to enter only through a small hole in a window shade (Figure 5). Young refocused the beam of light with mirrors and split the beam with a paper-thin card. The split light beams were then projected onto a screen, and formed an alternating light and dark banding pattern – that was a sign that light was indeed a wave (see our Light I: Particle or Wave? module).

Figure 5: Young's split-light beam experiment helped clarify the wave nature of light.

Approximately 100 years later, in 1905, new experiments led Albert Einstein to conclude that light exhibits properties of both waves and particles . Einstein's dual wave-particle theory is now generally accepted by scientists.

Experiments continue to help refine our understanding of light even today. In addition to his wave-particle theory , Einstein also proposed that the speed of light was unchanging and absolute. Yet in 1998 a group of scientists led by Lene Hau showed that light could be slowed from its normal speed of 3 x 10 8 meters per second to a mere 17 meters per second with a special experimental apparatus (Hau et al., 1999). The series of experiments that began with Alhazen 's work 1000 years ago has led to a progressively deeper understanding of the nature of light. Although the tools with which scientists conduct experiments may have become more complex, the principles behind controlled experiments are remarkably similar to those used by Pasteur and Alhazen hundreds of years ago.

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[ noun ik- sper - uh -m uh nt ; verb ek- sper - uh -ment ]

a chemical experiment; a teaching experiment; an experiment in living.

a product that is the result of long experiment.

Synonyms: investigation , research

• Obsolete. experience .

verb (used without object)

to experiment with a new procedure.

• a test or investigation, esp one planned to provide evidence for or against a hypothesis: a scientific experiment
• the act of conducting such an investigation or test; experimentation; research

a poetic experiment

• an obsolete word for experience
• intr to make an experiment or experiments

/ ĭk-spĕr ′ ə-mənt /

• A test or procedure carried out under controlled conditions to determine the validity of a hypothesis or make a discovery.
• See Note at hypothesis

Derived Forms

• exˈperiˌmenter , noun

Other Words From

• ex·peri·menter ex·peri·mentor ex·peri·men·tator noun
• preex·peri·ment noun
• proex·peri·ment adjective
• reex·peri·ment verb (used without object) noun
• unex·peri·mented adjective

Word History and Origins

Origin of experiment 1

Synonym Study

Example sentences.

IBM hopes that a platform like RoboRXN could dramatically speed up that process by predicting the recipes for compounds and automating experiments.

The hope there is for improved sensitivity in searches for dark matter or experiments that might reveal some long-sought flaws in our standard model of particle physics.

The experiment represents early progress toward the possible development of an ultra-secure communications network beamed from space.

The new experiment represents, however, the first time scientists have applied machine learning to “validation,” a further step toward confirming results that involves additional statistical calculation.

At first, the sites amounted to experiments on the outer edges of the crypto universe, but in 2020 they have started to attract real money.

To put it rather uncharitably, the USPHS practiced a major dental experiment on a city full of unconsenting subjects.

If the noble experiment of American democracy is to mean anything, it is fidelity to the principle of freedom.

A classroom experiment seeks to demonstrate what it looks like.

This video, courtesy of BuzzFeed, tries a bit of an experiment to get some answers.

In the fall of 1992, Booker became a vegetarian “as an experiment,” he said, “and I was surprised by how much my body took to it.”

With Bacon, experientia does not always mean observation; and may mean either experience or experiment.

I made the experiment two years ago, and all my experience since has corroborated the conclusion then arrived at.

But this is quite enough to justify the inconsiderable expense which the experiment I urge would involve.

He commenced to experiment in electro-pneumatics in the year 1860, and early in 1861 communicated his discoveries to Mr. Barker.

Readers will doubtless be familiar with the well-known experiment illustrating this point.

Related Words

• examination
• experimentation
• observation
• undertaking

Noun: a procedure done in a controlled environment for the purpose of gathering observations , data, or facts, demonstrating known facts or theories, or testing hypotheses or theories. Verb: to carry out such a procedure.

Last updated on May 29th, 2023

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• Independent/Dependent Variables

Definitions of Control, Constant, Independent and Dependent Variables in a Science Experiment

Why Should You Only Test for One Variable at a Time in an Experiment?

The point of an experiment is to help define the cause and effect relationships between components of a natural process or reaction. The factors that can change value during an experiment or between experiments, such as water temperature, are called scientific variables, while those that stay the same, such as acceleration due to gravity at a certain location, are called constants.

The scientific method includes three main types of variables: constants, independent, and dependent variables. In a science experiment, each of these variables define a different measured or constrained aspect of the system.

Constant Variables

Experimental constants are values that should not change either during or between experiments. Many natural forces and properties, such as the speed of light and the atomic weight of gold, are experimental constants. In some cases, a property can be considered constant for the purposes of an experiment even though it technically could change under certain circumstances. The boiling point of water changes with altitude and acceleration due to gravity decreases with distance from the earth, but for experiments in one location these can also be considered constants.

Sometimes also called a controlled variable. A constant is a variable that could change, but that the experimenter intentionally keeps constant in order to more clearly isolate the relationship between the independent variable and the dependent variable.

If extraneous variables are not properly constrained, they are referred to as confounding variables, as they interfere with the interpretation of the results of the experiment.

Some examples of control variables might be found with an experiment examining the relationship between the amount of sunlight plants receive (independent variable) and subsequent plant growth (dependent variable). The experiment should control the amount of water the plants receive and when, what type of soil they are planted in, the type of plant, and as many other different variables as possible. This way, only the amount of light is being changed between trials, and the outcome of the experiment can be directly applied to understanding only this relationship.

Independent Variable

The independent variable in an experiment is the variable whose value the scientist systematically changes in order to see what effect the changes have. A well-designed experiment has only one independent variable in order to maintain a fair test. If the experimenter were to change two or more variables, it would be harder to explain what caused the changes in the experimental results. For example, someone trying to find how quickly water boils could alter the volume of water or the heating temperature, but not both.

Dependent Variable

A dependent variable – sometimes called a responding variable – is what the experimenter observes to find the effect of systematically varying the independent variable. While an experiment may have multiple dependent variables, it is often wisest to focus the experiment on one dependent variable so that the relationship between it and the independent variable can be clearly isolated. For example, an experiment could examine how much sugar can dissolve in a set volume of water at various temperatures. The experimenter systematically alters temperature (independent variable) to see its effect on the quantity of dissolved sugar (dependent variable).

Control Groups

In some experiment designs, there might be one effect or manipulated variable that is being measured. Sometimes there might be one collection of measurements or subjects completely separated from this variable called the control group. These control groups are held as a standard to measure the results of a scientific experiment.

An example of such a situation might be a study regarding the effectiveness of a certain medication. There might be multiple experimental groups that receive the medication in varying doses and applications, and there would likely be a control group that does not receive the medication at all.

Representing Results

Identifying which variables are independent, dependent, and controlled helps to collect data, perform useful experiments, and accurately communicate results. When graphing or displaying data, it is crucial to represent data accurately and understandably. Typically, the independent variable goes on the x-axis, and the dependent variable goes on the y-axis.

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• ScienceBuddies.org: Variables in Your Science Fair Project

About the Author

Benjamin Twist has worked as a writer, editor and consultant since 2007. He writes fiction and nonfiction for online and print publications, as well as offering one-on-one writing consultations and tutoring. Twist holds a Master of Arts in Bible exposition from Columbia International University.

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Experiment in Physics

Physics, and natural science in general, is a reasonable enterprise based on valid experimental evidence, criticism, and rational discussion. It provides us with knowledge of the physical world, and it is experiment that provides the evidence that grounds this knowledge. Experiment plays many roles in science. One of its important roles is to test theories and to provide the basis for scientific knowledge. [ 1 ] It can also call for a new theory, either by showing that an accepted theory is incorrect, or by exhibiting a new phenomenon that is in need of explanation. Experiment can provide hints toward the structure or mathematical form of a theory and it can provide evidence for the existence of the entities involved in our theories. Finally, it may also have a life of its own, independent of theory. Scientists may investigate a phenomenon just because it looks interesting. Such experiments may provide evidence for a future theory to explain. [Examples of these different roles will be presented below.] As we shall see below, a single experiment may play several of these roles at once.

If experiment is to play these important roles in science then we must have good reasons to believe experimental results, for science is a fallible enterprise. Theoretical calculations, experimental results, or the comparison between experiment and theory may all be wrong. Science is more complex than “The scientist proposes, Nature disposes.” It may not always be clear what the scientist is proposing. Theories often need to be articulated and clarified. It also may not be clear how Nature is disposing. Experiments may not always give clear-cut results, and may even disagree for a time.

In what follows, the reader will find an epistemology of experiment, a set of strategies that provides reasonable belief in experimental results. Scientific knowledge can then be reasonably based on these experimental results.

1. Introduction: Epistemology of Experiment

2.1.1 representing and intervening, 2.1.2 experimental strategies, 2.1.3 complexity of experimental practice, 2.2.1 the experimenters’ regress, 2.2.2 communal opportunism and plastic resources, 2.2.3 critical responses, 2.2.4 the dance of agency, 2.2.5 hacking’s ‘the social construction of what’, 2.3 measuring, calibrating, predicting, 2.4 big science physics: theory-ladenness in high energy physics, 3.1 a life of its own, 3.2.1 a crucial experiment: the discovery of parity nonconservation, 3.2.2 a persuasive experiment: the discovery of cp violation, 3.2.3 confirmation after 70 years: the discovery of bose-einstein condensation, 3.3.1 the fall of the fifth force, 3.3.2 right experiment, wrong theory: the stern-gerlach experiment, 3.3.3 sometimes refutation doesn’t work: the double-scattering of electrons, 3.3.4 the failure to detect anomalies, 3.3.5 the ‘look elsewhere’ effect: discovering the higgs boson, 3.4.1 evidence for a new entity: j.j. thomson and the electron, 3.4.2 the articulation of theory: weak interactions, 4. experiment and observation, 5.1 epistemological strategies and the peppered moth experiment, 5.2 the meselson-stahl experiment: “the most beautiful experiment in biology”, 6. computer simulations and experimentation, 7. conclusion, principal works, other suggested reading, other internet resources, related entries.

Epistemology of experiment is a branch of philosophy of science focusing on the diverse roles that experiment plays in science, its various connections to theory, to the understanding and functions of experimental apparatus, and to the structure and culture of the scientific community in the laboratory setting. The epistemological analysis of experiments ranges from highly abstract philosophical arguments with only indirect connection to actual practice, to analysis immersed in reflective case studies. For a long time experiments in physics have been the leading edge of experimental science, pioneering experimental techniques, methods and innovative settings. This is why much of epistemology of experiment has focused on physics.

The 17th century witnessed the first philosophical reflections on the nature of experimentation. This should not be surprising given that experiment was emerging as a central scientific tool at the time. The aim of these reflections was to uncover why nature reveals its hidden aspects to us when we force experimental methods upon it.

Some natural philosophers believed that scientific knowledge was little more than the proper application of observational and experimental techniques on natural phenomena. Francis Bacon went so far as to claim that it was possible to perform what he called a crucial experiment (experimentum crucis), an ideal experiment of sorts that can determine alone which of two rival hypotheses is correct. And even some of the giants of modern science such as Newton subscribed to the view that scientific theories are directly induced from experimental results and observations without the help of untested hypotheses. It is little wonder, then, that many natural philosophers thought that experimental techniques and their proper application should be a primary object of philosophical study of science.

Yet not everybody agreed. Hobbes, for instance pointed out that human reason preceded experimental techniques and their application. He thought that human reasoning reveals to us the natural law, and criticized Boyle’s optimism regarding experimental method’s ability to reveal it (Shapin and Schaffer 1984). Doesn’t human reason guide experimenter’s actions, in the way it leads us to choose data and samples, and the way it allows us to interpret them, after all? If so, we should focus on the philosophical study of reason and theoretical scientific reasoning rather than on the study of experimental techniques and their applications.

This vigorous early debate in many ways anticipated the main points of disagreement in debates to come. Yet the philosophical interest in experimentation almost completely lost its steam at the end of the 19th century and did not recover until fairly late in the 20th century.

During that period philosophers turned much of their attention to the study of the logical structure of scientific theories and its connection to evidence. The tenets of logical positivism influenced this area of investigation — as well as philosophy more generally — at the time. One of these tenets stated that observational and theoretical propositions in science are separable. My readings of the gradation on the scale of a mercury thermometer can be separated from rather complicated theoretical statements concerning heat transfer and the theoretical concept of temperature.

In fact, not only can one separate theory and observation, but the former is considered justified only in light of its correspondence with the latter. The theory of heat transfer is confirmed by propositions originating in the kind of readings I perform on my mercury thermometer. Thus, observational propositions are simply a result of an experiment or a set of observations a scientist performs in order to confirm or refute a theory.

Thomas Kuhn and Paul Feyerabend vigorously criticized this view. They argued that observations and experimental results are already part of a theoretical framework and thus cannot confirm a theory independently. Nor there is a theory-neutral language for capturing observations. Even a simple reading of a mercury thermometer inevitably depends on a theoretically-charged concept of temperature. In short, the evidence is always theory-laden.

Yet neither the proponents of logical positivism nor their critics ever attempted to explain the nature of experimentation that produces all-important observational statements. And the reason for this was very simple: they didn’t think that there was anything interesting to explain. Their views on the relationship between theory and evidence were diametrically opposed, but they all found only the final product of experimentation, namely observational statements, philosophically interesting. As a result, the experimental process itself was set aside in their philosophical study of science. This has gradually changed only with the advent of New Experimentalism, with Ian Hacking’s work at its forefront.

2. Experimental Results

2.1 learning from experiment.

It has been more than three decades since Ian Hacking asked, “Do we see through a microscope?” (Hacking 1981). Hacking’s question really asked how do we come to believe in an experimental result obtained with a complex experimental apparatus? How do we distinguish between a valid result [ 2 ] and an artifact created by that apparatus? If experiment is to play all of the important roles in science mentioned above and to provide the evidential basis for scientific knowledge, then we must have good reasons to believe in those results. Hacking provided an extended answer in the second half of Representing and Intervening (1983). He pointed out that even though an experimental apparatus is laden with, at the very least, the theory of the apparatus, observations remain robust despite changes in the theory of the apparatus or in the theory of the phenomenon. His illustration was the sustained belief in microscope images despite the major change in the theory of the microscope when Abbe pointed out the importance of diffraction in its operation. One reason Hacking gave for this is that in making such observations the experimenters intervened—they manipulated the object under observation. Thus, in looking at a cell through a microscope, one might inject fluid into the cell or stain the specimen. One expects the cell to change shape or color when this is done. Observing the predicted effect strengthens our belief in both the proper operation of the microscope and in the observation. This is true in general. Observing the predicted effect of an intervention strengthens our belief in both the proper operation of the experimental apparatus and in the observations made with it.

Hacking also discussed the strengthening of one’s belief in an observation by independent confirmation. The fact that the same pattern of dots—dense bodies in cells—is seen with “different” microscopes, (e.g. ordinary, polarizing, phase-contrast, fluorescence, interference, electron, acoustic etc.) argues for the validity of the observation. One might question whether “different” is a theory-laden term. After all, it is our theory of light and of the microscope that allows us to consider these microscopes as different from each other. Nevertheless, the argument holds. Hacking correctly argues that it would be a preposterous coincidence if the same pattern of dots were produced in two totally different kinds of physical systems. Different apparatuses have different backgrounds and systematic errors, making the coincidence, if it is an artifact, most unlikely. If it is a correct result, and the instruments are working properly, the coincidence of results is understandable.

Hacking’s answer is correct as far as it goes. It is, however, incomplete. What happens when one can perform the experiment with only one type of apparatus, such as an electron microscope or a radio telescope, or when intervention is either impossible or extremely difficult? Other strategies are needed to validate the observation. [ 3 ] These may include:

• Experimental checks and calibration, in which the experimental apparatus reproduces known phenomena. For example, if we wish to argue that the spectrum of a substance obtained with a new type of spectrometer is correct, we might check that this new spectrometer could reproduce the known Balmer series in hydrogen. If we correctly observe the Balmer Series then we strengthen our belief that the spectrometer is working properly. This also strengthens our belief in the results obtained with that spectrometer. If the check fails then we have good reason to question the results obtained with that apparatus.
• Reproducing artifacts that are known in advance to be present. An example of this comes from experiments to measure the infrared spectra of organic molecules (Randall et al. 1949). It was not always possible to prepare a pure sample of such material. Sometimes the experimenters had to place the substance in an oil paste or in solution. In such cases, one expects to observe the spectrum of the oil or the solvent, superimposed on that of the substance. One can then compare the composite spectrum with the known spectrum of the oil or the solvent. Observation then of this artifact gives confidence in other measurements made with the spectrometer.
• Elimination of plausible sources of error and alternative explanations of the result (the Sherlock Holmes strategy). [ 4 ] Thus, when scientists claimed to have observed electric discharges in the rings of Saturn, they argued for their result by showing that it could not have been caused by defects in the telemetry, interaction with the environment of Saturn, lightning, or dust. The only remaining explanation of their result was that it was due to electric discharges in the rings—there was no other plausible explanation of the observation. (In addition, the same result was observed by both Voyager 1 and Voyager 2. This provided independent confirmation. Often, several epistemological strategies are used in the same experiment.)
• Using the results themselves to argue for their validity. Consider the problem of Galileo’s telescopic observations of the moons of Jupiter. Although one might very well believe that his primitive, early telescope might have produced spurious spots of light, it is extremely implausible that the telescope would create images that they would appear to be a eclipses and other phenomena consistent with the motions of a small planetary system. It would have been even more implausible to believe that the created spots would satisfy Kepler’s Third Law (\(\bfrac{R^3}{T^2} = \) constant). A similar argument was used by Robert Millikan to support his observation of the quantization of electric charge and his measurement of the charge of the electron. Millikan remarked, “The total number of changes which we have observed would be between one and two thousand, and in not one single instance has there been any change which did not represent the advent upon the drop of one definite invariable quantity of electricity or a very small multiple of that quantity ” (Millikan 1911, p. 360). In both of these cases one is arguing that there was no plausible malfunction of the apparatus, or background, that would explain the observations.
• Using an independently well-corroborated theory of the phenomena to explain the results. This was illustrated in the discovery of the \(\ce{W^{\pm}}\), the charged intermediate vector boson required by the Weinberg-Salam unified theory of electroweak interactions. Although these experiments used very complex apparatuses and used other epistemological strategies (for details see Franklin 1986, pp. 170–72). I believe that the agreement of the observations with the theoretical predictions of the particle properties helped to validate the experimental results. In this case the particle candidates were observed in events that contained an electron with high transverse momentum and in which there were no particle jets, just as predicted by the theory. In addition, the measured particle mass of \(81\pm 5\) GeV/c 2 and \(80^{+10}_{-6}\) GeV/c 2 , found in the two experiments (note the independent confirmation also), was in good agreement with the theoretical prediction of \(82\pm 2.4\) GeV/c 2 . It was very improbable that any background effect, which might mimic the presence of the particle, would be in agreement with theory.
• Using an apparatus based on a well-corroborated theory. In this case the support for the theory inspires confidence in the apparatus based on that theory. This is the case with the electron microscope and the radio telescope, whose operations are based on a well-supported theories, although other strategies are also used to validate the observations made with these instruments.
• Using statistical arguments. An interesting example of this arose in the 1960s when the search for new particles and resonances occupied a substantial fraction of the time and effort of those physicists working in experimental high-energy physics. The usual technique was to plot the number of events observed as a function of the invariant mass of the final-state particles and to look for bumps above a smooth background. The usual informal criterion for the presence of a new particle was that it resulted in a three standard-deviation effect above the background, a result that had a probability of 0.27% of occurring in a single bin. This criterion was later changed to four standard deviations, which had a probability of 0.0064% when it was pointed out that the number of graphs plotted each year by high-energy physicists made it rather probable, on statistical grounds, that a three standard-deviation effect would be observed.

These strategies along with Hacking’s intervention and independent confirmation constitute an epistemology of experiment. They provide us with good reasons for belief in experimental results, They do not, however, guarantee that the results are correct. There are many experiments in which these strategies are applied, but whose results are later shown to be incorrect (examples will be presented below). Experiment is fallible. Neither are these strategies exclusive or exhaustive. No single one of them, or fixed combination of them, guarantees the validity of an experimental result. Physicists use as many of the strategies as they can conveniently apply in any given experiment.

In How Experiments End (1987), Peter Galison extended the discussion of experiment to more complex situations. In his histories of the measurements of the gyromagnetic ratio of the electron, the discovery of the muon, and the discovery of weak neutral currents, he considered a series of experiments measuring a single quantity, a set of different experiments culminating in a discovery, and two high- energy physics experiments performed by large groups with complex experimental apparatus.

Galison’s view is that experiments end when the experimenters believe that they have a result that will stand up in court—a result that I believe includes the use of the epistemological strategies discussed earlier. Thus, David Cline, one of the weak neutral-current experimenters remarked, “At present I don’t see how to make these effects [the weak neutral current event candidates] go away” (Galison, 1987, p. 235).

Galison emphasizes that, within a large experimental group, different members of the group may find different pieces of evidence most convincing. Thus, in the Gargamelle weak neutral current experiment, several group members found the single photograph of a neutrino-electron scattering event particularly important, whereas for others the difference in spatial distribution between the observed neutral current candidates and the neutron background was decisive. Galison attributes this, in large part, to differences in experimental traditions, in which scientists develop skill in using certain types of instruments or apparatus. In particle physics, for example, there is the tradition of visual detectors, such as the cloud chamber or the bubble chamber, in contrast to the electronic tradition of Geiger and scintillation counters and spark chambers. Scientists within the visual tradition tend to prefer “golden events” that clearly demonstrate the phenomenon in question, whereas those in the electronic tradition tend to find statistical arguments more persuasive and important than individual events. (For further discussion of this issue see Galison 1997).

Galison points out that major changes in theory and in experimental practice and instruments do not necessarily occur at the same time. This persistence of experimental results provides continuity across these conceptual changes. Thus, the experiments on the gyromagnetic ratio spanned classical electromagnetism, Bohr’s old quantum theory, and the new quantum mechanics of Heisenberg and Schrodinger. Robert Ackermann has offered a similar view in his discussion of scientific instruments.

The advantages of a scientific instrument are that it cannot change theories. Instruments embody theories, to be sure, or we wouldn’t have any grasp of the significance of their operation….Instruments create an invariant relationship between their operations and the world, at least when we abstract from the expertise involved in their correct use. When our theories change, we may conceive of the significance of the instrument and the world with which it is interacting differently, and the datum of an instrument may change in significance, but the datum can nonetheless stay the same, and will typically be expected to do so. An instrument reads 2 when exposed to some phenomenon. After a change in theory, [ 5 ] it will continue to show the same reading, even though we may take the reading to be no longer important, or to tell us something other than what we thought originally (Ackermann 1985, p. 33).

Galison also discusses other aspects of the interaction between experiment and theory. Theory may influence what is considered to be a real effect, demanding explanation, and what is considered background. In his discussion of the discovery of the muon, he argues that the calculation of Oppenheimer and Carlson, which showed that showers were to be expected in the passage of electrons through matter, left the penetrating particles, later shown to be muons, as the unexplained phenomenon. Prior to their work, physicists thought the showering particles were the problem, whereas the penetrating particles seemed to be understood.

The role of theory as an “enabling theory,” (i.e., one that allows calculation or estimation of the size of the expected effect and also the size of expected backgrounds) is also discussed by Galison. (See also (Franklin 1995) and the discussion of the Stern-Gerlach experiment below). Such a theory can help to determine whether an experiment is feasible. Galison also emphasizes that elimination of background that might simulate or mask an effect is central to the experimental enterprise, and not a peripheral activity. In the case of the weak neutral current experiments, the existence of the currents depended crucially on showing that the event candidates could not all be due to neutron background. [ 6 ]

There is also a danger that the design of an experiment may preclude observation of a phenomenon. Galison points out that the original design of one of the neutral current experiments, which included a muon trigger, would not have allowed the observation of neutral currents. In its original form the experiment was designed to observe charged currents, which produce a high energy muon. Neutral currents do not. Therefore, having a muon trigger precluded their observation. Only after the theoretical importance of the search for neutral currents was emphasized to the experimenters was the trigger changed. Changing the design did not, of course, guarantee that neutral currents would be observed.

Galison also shows that the theoretical presuppositions of the experimenters may enter into the decision to end an experiment and report the result. Einstein and de Haas ended their search for systematic errors when their value for the gyromagnetic ratio of the electron, \(g = 1\), agreed with their theoretical model of orbiting electrons. This effect of presuppositions might cause one to be skeptical of both experimental results and their role in theory evaluation. Galison’s history shows, however, that, in this case, the importance of the measurement led to many repetitions of the measurement. This resulted in an agreed-upon result that disagreed with theoretical expectations.

Galison has eventually modified his views. In Image and Logic , an extended study of instrumentation in 20th-century high-energy physics, Galison (1997) has extended his argument that there are two distinct experimental traditions within that field—the visual (or image) tradition and the electronic (or logic) tradition. The image tradition uses detectors such as cloud chambers or bubble chambers, which provide detailed and extensive information about each individual event. The electronic detectors used by the logic tradition, such as geiger counters, scintillation counters, and spark chambers, provide less detailed information about individual events, but detect more events. Galison’s view is that experimenters working in these two traditions form distinct epistemic and linguistic groups that rely on different forms of argument. The visual tradition emphasizes the single “golden” event. “On the image side resides a deep-seated commitment to the ‘golden event’: the single picture of such clarity and distinctness that it commands acceptance.” (Galison, 1997, p. 22) “The golden event was the exemplar of the image tradition: an individual instance so complete and well defined, so ‘manifestly’ free of distortion and background that no further data had to be involved” (p. 23). Because the individual events provided in the logic detectors contained less detailed information than the pictures of the visual tradition, statistical arguments based on large numbers of events were required.

Kent Staley (1999) disagrees. He argues that the two traditions are not as distinct as Galison believes:

I show that discoveries in both traditions have employed the same statistical [I would add “and/or probabilistic”] form of argument, even when basing discovery claims on single, golden events. Where Galison sees an epistemic divide between two communities that can only be bridged by creole- or pidgin-like ‘interlanguage,’ there is in fact a shared commitment to a statistical form of experimental argument. (p. 96).

Staley believes that although there is certainly epistemic continuity within a given tradition, there is also a continuity between the traditions. This does not, I believe, mean that the shared commitment comprises all of the arguments offered in any particular instance, but rather that the same methods are often used by both communities. Galison does not deny that statistical methods are used in the image tradition, but he thinks that they are relatively unimportant. “While statistics could certainly be used within the image tradition, it was by no means necessary for most applications” (Galison, 1997, p. 451). In contrast, Galison believes that arguments in the logic tradition “were inherently and inalienably statistical. Estimation of probable errors and the statistical excess over background is not a side issue in these detectors—it is central to the possibility of any demonstration at all” (p. 451).

Although a detailed discussion of the disagreement between Staley and Galison would take us too far from the subject of this essay, they both agree that arguments are offered for the correctness of experimental results. Their disagreement concerns the nature of those arguments. (For further discussion see Franklin, (2002), pp. 9–17).

2.2 The Case Against Learning From Experiment

H. Collins, A. Pickering, and others, have raised objections to the view that experimental results are accepted on the basis of epistemological arguments. They point out that “a sufficiently determined critic can always find a reason to dispute any alleged ‘result’” (MacKenzie 1989, p. 412). Harry Collins, for example, is well known for his skepticism concerning both experimental results and evidence. He develops an argument that he calls the “experimenters’ regress” (Collins 1985, chapter 4, pp. 79–111): What scientists take to be a correct result is one obtained with a good, that is, properly functioning, experimental apparatus. But a good experimental apparatus is simply one that gives correct results. Collins claims that there are no formal criteria that one can apply to decide whether or not an experimental apparatus is working properly. In particular, he argues that calibrating an experimental apparatus by using a surrogate signal cannot provide an independent reason for considering the apparatus to be reliable.

In Collins’ view the regress is eventually broken by negotiation within the appropriate scientific community, a process driven by factors such as the career, social, and cognitive interests of the scientists, and the perceived utility for future work, but one that is not decided by what we might call epistemological criteria, or reasoned judgment. Thus, Collins concludes that his regress raises serious questions concerning both experimental evidence and its use in the evaluation of scientific hypotheses and theories. Indeed, if no way out of the regress can be found, then he has a point.

Collins strongest candidate for an example of the experimenters’ regress is presented in his history of the early attempts to detect gravitational radiation, or gravity waves. (For more detailed discussion of this episode see (Collins 1985; 1994; Franklin 1994; 1997a) In this case, the physics community was forced to compare J. Weber’s claims that he had observed gravity waves with the reports from six other experiments that failed to detect them. On the one hand, Collins argues that the decision between these conflicting experimental results could not be made on epistemological or methodological grounds—he claims that the six negative experiments could not legitimately be regarded as replications [ 7 ] and hence become less impressive. On the other hand, Weber’s apparatus, precisely because the experiments used a new type of apparatus to try to detect a hitherto unobserved phenomenon, [ 8 ] could not be subjected to standard calibration techniques.

The results presented by Weber’s critics were not only more numerous, but they had also been carefully cross-checked. The groups had exchanged both data and analysis programs and confirmed their results. The critics had also investigated whether or not their analysis procedure, the use of a linear algorithm, could account for their failure to observe Weber’s reported results. They had used Weber’s preferred procedure, a nonlinear algorithm, to analyze their own data, and still found no sign of an effect. They had also calibrated their experimental apparatuses by inserting acoustic pulses of known energy and finding that they could detect a signal. Weber, on the other hand, as well as his critics using his analysis procedure, could not detect such calibration pulses.

There were, in addition, several other serious questions raised about Weber’s analysis procedures. These included an admitted programming error that generated spurious coincidences between Weber’s two detectors, possible selection bias by Weber, Weber’s report of coincidences between two detectors when the data had been taken four hours apart, and whether or not Weber’s experimental apparatus could produce the narrow coincidences claimed.

It seems clear that the critics’ results were far more credible than Weber’s. They had checked their results by independent confirmation, which included the sharing of data and analysis programs. They had also eliminated a plausible source of error, that of the pulses being longer than expected, by analyzing their results using the nonlinear algorithm and by explicitly searching for such long pulses. [ 9 ] They had also calibrated their apparatuses by injecting pulses of known energy and observing the output.

Contrary to Collins, I believe that the scientific community made a reasoned judgment and rejected Weber’s results and accepted those of his critics. Although no formal rules were applied (e.g. if you make four errors, rather than three, your results lack credibility; or if there are five, but not six, conflicting results, your work is still credible) the procedure was reasonable.

Pickering has argued that the reasons for accepting results are the future utility of such results for both theoretical and experimental practice and the agreement of such results with the existing community commitments. In discussing the discovery of weak neutral currents, Pickering states,

Quite simply, particle physicists accepted the existence of the neutral current because they could see how to ply their trade more profitably in a world in which the neutral current was real. (1984b, p. 87) Scientific communities tend to reject data that conflict with group commitments and, obversely, to adjust their experimental techniques to tune in on phenomena consistent with those commitments. (1981, p. 236)

The emphasis on future utility and existing commitments is clear. These two criteria do not necessarily agree. For example, there are episodes in the history of science in which more opportunity for future work is provided by the overthrow of existing theory. (See, for example, the history of the overthrow of parity conservation and of CP symmetry discussed below and in Franklin 1986, Ch. 1, 3.)

Pickering offered a different view of experimental results in late 1980s. In his view the material procedure (including the experimental apparatus itself along with setting it up, running it, and monitoring its operation), the theoretical model of that apparatus, and the theoretical model of the phenomena under investigation are all plastic resources that the investigator brings into relations of mutual support. (Pickering 1987; Pickering 1989). He says:

Achieving such relations of mutual support is, I suggest, the defining characteristic of the successful experiment. (1987, p. 199)

He uses Morpurgo’s search for free quarks, or fractional charges of \(\tfrac{1}{3} e\) or \(\tfrac{2}{3} e\), where \(e\) is the charge of the electron. (See also Gooding 1992.) Morpurgo used a modern Millikan-type apparatus and initially found a continuous distribution of charge values. Following some tinkering with the apparatus, Morpurgo found that if he separated the capacitor plates he obtained only integral values of charge. “After some theoretical analysis, Morpurgo concluded that he now had his apparatus working properly, and reported his failure to find any evidence for fractional charges” (Pickering 1987, p. 197).

Pickering goes on to note that Morpurgo did not tinker with the two competing theories of the phenomena then on offer, those of integral and fractional charge:

The initial source of doubt about the adequacy of the early stages of the experiment was precisely the fact that their findings—continuously distributed charges—were consonant with neither of the phenomenal models which Morpurgo was prepared to countenance. And what motivated the search for a new instrumental model was Morpurgo’s eventual success in producing findings in accordance with one of the phenomenal models he was willing to accept The conclusion of Morpurgo’s first series of experiments, then, and the production of the observation report which they sustained, was marked by bringing into relations of mutual support of the three elements I have discussed: the material form of the apparatus and the two conceptual models, one instrumental and the other phenomenal. Achieving such relations of mutual support is, I suggest, the defining characteristic of the successful experiment. (p. 199)

Pickering has made several important and valid points concerning experiment. Most importantly, he has emphasized that an experimental apparatus is initially rarely capable of producing a valid experimental results and that some adjustment, or tinkering, is required before it does. He has also recognized that both the theory of the apparatus and the theory of the phenomena can enter into the production of a valid experimental result. What one may question, however, is the emphasis he places on these theoretical components. From Millikan onwards, experiments had strongly supported the existence of a fundamental unit of charge and charge quantization. The failure of Morpurgo’s apparatus to produce measurements of integral charge indicated that it was not operating properly and that his theoretical understanding of it was faulty. It was the failure to produce measurements in agreement with what was already known (i.e., the failure of an important experimental check) that caused doubts about Morpurgo’s measurements. This was true regardless of the theoretical models available, or those that Morpurgo was willing to accept. It was only when Morpurgo’s apparatus could reproduce known measurements that it could be trusted and used to search for fractional charge. To be sure, Pickering has allowed a role for the natural world in the production of the experimental result, but it does not seem to be decisive.

Ackermann has offered a modification of Pickering’s view. He suggests that the experimental apparatus itself is a less plastic resource then either the theoretical model of the apparatus or that of the phenomenon.

To repeat, changes in \(A\) [the apparatus] can often be seen (in real time, without waiting for accommodation by \(B\) [the theoretical model of the apparatus]) as improvements, whereas ‘improvements’ in \(B\) don’t begin to count unless \(A\) is actually altered and realizes the improvements conjectured. It’s conceivable that this small asymmetry can account, ultimately, for large scale directions of scientific progress and for the objectivity and rationality of those directions. (Ackermann 1991, p. 456)

Hacking (1992) has also offered a more complex version of Pickering’s later view. He suggests that the results of mature laboratory science achieve stability and are self-vindicating when the elements of laboratory science are brought into mutual consistency and support. These are (1) ideas: questions, background knowledge, systematic theory, topical hypotheses, and modeling of the apparatus; (2) things: target, source of modification, detectors, tools, and data generators; and (3) marks and the manipulation of marks: data, data assessment, data reduction, data analysis, and interpretation.

Stable laboratory science arises when theories and laboratory equipment evolve in such a way that they match each other and are mutually self-vindicating. (1992, p. 56) We invent devices that produce data and isolate or create phenomena, and a network of different levels of theory is true to these phenomena. Conversely we may in the end count them only as phenomena only when the data can be interpreted by theory. (pp. 57–8)

One might ask whether such mutual adjustment between theory and experimental results can always be achieved? What happens when an experimental result is produced by an apparatus on which several of the epistemological strategies, discussed earlier, have been successfully applied, and the result is in disagreement with our theory of the phenomenon? Accepted theories can be refuted. Several examples will be presented below.

Hacking himself worries about what happens when a laboratory science that is true to the phenomena generated in the laboratory, thanks to mutual adjustment and self-vindication, is successfully applied to the world outside the laboratory. Does this argue for the truth of the science. In Hacking’s view it does not. If laboratory science does produce happy effects in the “untamed world,… it is not the truth of anything that causes or explains the happy effects” (1992, p. 60).

Pickering offered yet another, a somewhat revised account of science. “My basic image of science is a performative one, in which the performances the doings of human and material agency come to the fore. Scientists are human agents in a field of material agency which they struggle to capture in machines (Pickering, 1995, p. 21).” He then discusses the complex interaction between human and material agency, which I interpret as the interaction between experimenters, their apparatus, and the natural world.

The dance of agency, seen asymmetrically from the human end, thus takes the form of a dialectic of resistance and accommodations, where resistance denotes the failure to achieve an intended capture of agency in practice, and accommodation an active human strategy of response to resistance, which can include revisions to goals and intentions as well as to the material form of the machine in question and to the human frame of gestures and social relations that surround it (p. 22).“

Pickering’s idea of resistance is illustrated by Morpurgo’s observation of continuous, rather than integral or fractional, electrical charge, which did not agree with his expectations. Morpurgo’s accommodation consisted of changing his experimental apparatus by using a larger separation between his plates, and also by modifying his theoretical account of the apparatus. That being done, integral charges were observed and the result stabilized by the mutual agreement of the apparatus, the theory of the apparatus, and the theory of the phenomenon. Pickering notes that ”the outcomes depend on how the world is (p. 182).“ ”In this way, then, how the material world is leaks into and infects our representations of it in a nontrivial and consequential fashion. My analysis thus displays an intimate and responsive engagement between scientific knowledge and the material world that is integral to scientific practice (p. 183).“

Nevertheless there is something confusing about Pickering’s invocation of the natural world. Although Pickering acknowledges the importance of the natural world, his use of the term ”infects“ seems to indicate that he isn’t entirely happy with this. Nor does the natural world seem to have much efficacy. It never seems to be decisive in any of Pickering’s case studies. Recall that he argued that physicists accepted the existence of weak neutral currents because ”they could ply their trade more profitably in a world in which the neutral current was real.“ In his account, Morpurgo’s observation of continuous charge is important only because it disagrees with his theoretical models of the phenomenon. The fact that it disagreed with numerous previous observations of integral charge doesn’t seem to matter. This is further illustrated by Pickering’s discussion of the conflict between Morpurgo and Fairbank. As we have seen, Morpurgo reported that he did not observe fractional electrical charges. On the other hand, in the late 1970s and early 1980s, Fairbank and his collaborators published a series of papers in which they claimed to have observed fractional charges (See, for example, LaRue, Phillips et al. 1981 ). Faced with this discord Pickering concludes,

In Chapter 3, I traced out Morpurgo’s route to his findings in terms of the particular vectors of cultural extension that he pursued, the particular resistances and accommodations thus precipitated, and the particular interactive stabilizations he achieved. The same could be done, I am sure, in respect of Fairbank. And these tracings are all that needs to said about their divergence. It just happened that the contingencies of resistance and accommodation worked out differently in the two instances. Differences like these are, I think, continually bubbling up in practice, without any special causes behind them (pp. 211–212).

The natural world seems to have disappeared from Pickering’s account. There is a real question here as to whether or not fractional charges exist in nature. The conclusions reached by Fairbank and by Morpurgo about their existence cannot both be correct. It seems insufficient to merely state, as Pickering does, that Fairbank and Morpurgo achieved their individual stabilizations and to leave the conflict unresolved. (Pickering does comment that one could follow the subsequent history and see how the conflict was resolved, and he does give some brief statements about it, but its resolution is not important for him). At the very least one should consider the actions of the scientific community. Scientific knowledge is not determined individually, but communally. Pickering seems to acknowledge this. ”One might, therefore, want to set up a metric and say that items of scientific knowledge are more or less objective depending on the extent to which they are threaded into the rest of scientific culture, socially stabilized over time, and so on. I can see nothing wrong with thinking this way…. (p. 196).“ The fact that Fairbank believed in the existence of fractional electrical charges, or that Weber strongly believed that he had observed gravity waves, does not make them right. These are questions about the natural world that can be resolved. Either fractional charges and gravity waves exist or they don’t, or to be more cautious we might say that we have good reasons to support our claims about their existence, or we do not.

Another issue neglected by Pickering is the question of whether a particular mutual adjustment of theory, of the apparatus or the phenomenon, and the experimental apparatus and evidence is justified. Pickering seems to believe that any such adjustment that provides stabilization, either for an individual or for the community, is acceptable. Others disagree. They note that experimenters sometimes exclude data and engage in selective analysis procedures in producing experimental results. These practices are, at the very least, questionable as is the use of the results produced by such practices in science. There are, in fact, procedures in the normal practice of science that provide safeguards against them. (For details see Franklin, 2002, Section 1).

The difference in attitudes toward the resolution of discord is one of the important distinctions between Pickering’s and Franklin’s view of science. Franklin remarks that it is insufficient simply to say that the resolution is socially stabilized. The important question is how that resolution was achieved and what were the reasons offered for that resolution. If we are faced with discordant experimental results and both experimenters have offered reasonable arguments for their correctness, then clearly more work is needed. It seems reasonable, in such cases, for the physics community to search for an error in one, or both, of the experiments.

Pickering discusses yet another difference between his view and that of Franklin. Pickering sees traditional philosophy of science as regarding objectivity ”as stemming from a peculiar kind of mental hygiene or policing of thought. This police function relates specifically to theory choice in science, which,… is usually discussed in terms of the rational rules or methods responsible for closure in theoretical debate (p. 197).“ He goes on to remark that,

The most action in recent methodological thought has centered on attempts like Allan Franklin’s to extend the methodological approach to experiments by setting up a set of rules for their proper performance. Franklin thus seeks to extend classical discussions of objectivity to the empirical base of science (a topic hitherto neglected in the philosophical tradition but one that, of course the mangle [Pickering’s view] also addresses). For an argument between myself and Franklin on the same lines as that laid out below, see (Franklin 1990, Chapter 8; Franklin 1991); and (Pickering 1991); and for commentaries related to that debate, (Ackermann 1991) and (Lynch 1991) (p. 197).”

For further discussion see Franklin 1993b. Although Franklin’s epistemology of experiment is designed to offer good reasons for belief in experimental results, they are not a set of rules. Franklin regards them as a set of strategies, from which physicists choose, in order to argue for the correctness of their results. As noted above, the strategies offered are neither exclusive or exhaustive.

There is another point of disagreement between Pickering and Franklin. Pickering claims to be dealing with the practice of science, and yet he excludes certain practices from his discussions. One scientific practice is the application of the epistemological strategies outlined above to argue for the correctness of an experimental results. In fact, one of the essential features of an experimental paper is the presentation of such arguments. Writing such papers, a performative act, is also a scientific practice and it would seem reasonable to examine both the structure and content of those papers.

Ian Hacking (1999, chapter 3) provided an incisive and interesting discussion of the issues that divide the constructivists (Collins, Pickering, etc.) from the rationalists (Stuewer, Franklin, Buchwald, etc.). He sets out three sticking points between the two views: 1) contingency, 2) nominalism, and 3) external explanations of stability.

Contingency is the idea that science is not predetermined, that it could have developed in any one of several successful ways. This is the view adopted by constructivists. Hacking illustrates this with Pickering’s account of high-energy physics during the 1970s during which the quark model came to dominate. (See Pickering 1984a).

The constructionist maintains a contingency thesis. In the case of physics, (a) physics theoretical, experimental, material) could have developed in, for example, a nonquarky way, and, by the detailed standards that would have evolved with this alternative physics, could have been as successful as recent physics has been by its detailed standards. Moreover, (b) there is no sense in which this imagined physics would be equivalent to present physics. The physicist denies that. (Hacking 1999, pp. 78–79). To sum up Pickering’s doctrine: there could have been a research program as successful (“progressive”) as that of high-energy physics in the 1970s, but with different theories, phenomenology, schematic descriptions of apparatus, and apparatus, and with a different, and progressive, series of robust fits between these ingredients. Moreover and this is something badly in need of clarification the “different” physics would not have been equivalent to present physics. Not logically incompatible with, just different. The constructionist about (the idea) of quarks thus claims that the upshot of this process of accommodation and resistance is not fully predetermined. Laboratory work requires that we get a robust fit between apparatus, beliefs about the apparatus, interpretations and analyses of data, and theories. Before a robust fit has been achieved, it is not determined what that fit will be. Not determined by how the world is, not determined by technology now in existence, not determined by the social practices of scientists, not determined by interests or networks, not determined by genius, not determined by anything (pp. 72–73, emphasis added).

Much depends here on what Hacking means by “determined.” If he means entailed then one must agree with him. It is doubtful that the world, or more properly, what we can learn about it, entails a unique theory. If not, as seems more plausible, he means that the way the world is places no restrictions on that successful science, then the rationalists disagree strongly. They want to argue that the way the world is restricts the kinds of theories that will fit the phenomena, the kinds of apparatus we can build, and the results we can obtain with such apparatuses. To think otherwise seems silly. Consider a homey example. It seems highly unlikely that someone can come up with a successful theory in which objects whose density is greater than that of air fall upwards. This is not a caricature of the view Hacking describes. Describing Pickering’s view, he states, “Physics did not need to take a route that involved Maxwell’s Equations, the Second Law of Thermodynamics, or the present values of the velocity of light (p. 70).” Although one may have some sympathy for this view as regards Maxwell’s Equations or the Second Law of Thermodynamics, one may not agree about the value of the speed of light. That is determined by the way the world is. Any successful theory of light must give that value for its speed.

At the other extreme are the “inevitablists,” among whom Hacking classifies most scientists. He cites Sheldon Glashow, a Nobel Prize winner, “Any intelligent alien anywhere would have come upon the same logical system as we have to explain the structure of protons and the nature of supernovae (Glashow 1992, p. 28).”

Another difference between Pickering and Franklin on contingency concerns the question of not whether an alternative is possible, but rather whether there are reasons why that alternative should be pursued. Pickering seems to identify can with ought .

In the late 1970s there was a disagreement between the results of low-energy experiments on atomic parity violation (the violation of left-right symmetry) performed at the University of Washington and at Oxford University and the result of a high-energy experiment on the scattering of polarized electrons from deuterium (the SLAC E122 experiment). The atomic-parity violation experiments failed to observe the parity-violating effects predicted by the Weinberg- Salam (W-S) unified theory of electroweak interactions, whereas the SLAC experiment observed the predicted effect. These early atomic physics results were quite uncertain in themselves and that uncertainty was increased by positive results obtained in similar experiments at Berkeley and Novosibirsk. At the time the theory had other evidential support, but was not universally accepted. Pickering and Franklin are in agreement that the W-S theory was accepted on the basis of the SLAC E122 result. They differ dramatically in their discussions of the experiments. Their difference on contingency concerns a particular theoretical alternative that was proposed at the time to explain the discrepancy between the experimental results.

Pickering asked why a theorist might not have attempted to find a variant of electroweak gauge theory that might have reconciled the Washington-Oxford atomic parity results with the positive E122 result. (What such a theorist was supposed to do with the supportive atomic parity results later provided by experiments at Berkeley and at Novosibirsk is never mentioned). “But though it is true that E122 analysed their data in a way that displayed the improbability [the probability of the fit to the hybrid model was 6 × 10 −4 ] of a particular class of variant gauge theories, the so-called ‘hybrid models,’ I do not believe that it would have been impossible to devise yet more variants” (Pickering 1991, p. 462). Pickering notes that open-ended recipes for constructing such variants had been written down as early as 1972 (p. 467). It would have been possible to do so, but one may ask whether or not a scientist might have wished to do so. If the scientist agreed with Franklin’s view that the SLAC E122 experiment provided considerable evidential weight in support of the W-S theory and that a set of conflicting and uncertain results from atomic parity-violation experiments gave an equivocal answer on that support, what reason would they have had to invent an alternative?

This is not to suggest that scientists do not, or should not, engage in speculation, but rather that there was no necessity to do so in this case. Theorists often do propose alternatives to existing, well-confirmed theories.

Constructivist case studies always seem to result in the support of existing, accepted theory (Pickering 1984a; 1984b; 1991; Collins 1985; Collins and Pinch 1993). One criticism implied in such cases is that alternatives are not considered, that the hypothesis space of acceptable alternatives is either very small or empty. One may seriously question this. Thus, when the experiment of Christenson et al . (1964) detected \(\ce{K2^0}\) decay into two pions, which seemed to show that CP symmetry (combined particle-antiparticle and space inversion symmetry) was violated, no fewer than 10 alternatives were offered. These included (1) the cosmological model resulting from the local dysymmetry of matter and antimatter, (2) external fields, (3) the decay of the \(\ce{K2^0}\) into a \(\ce{K1^0}\) with the subsequent decay of the \(\ce{K1^0}\)into two pions, which was allowed by the symmetry, (4) the emission of another neutral particle, “the paritino,” in the \(\ce{K2^0}\) decay, similar to the emission of the neutrino in beta decay, (5) that one of the pions emitted in the decay was in fact a “spion,” a pion with spin one rather than zero, (6) that the decay was due to another neutral particle, the L, produced coherently with the \(\ce{K^0}\), (7) the existence of a “shadow” universe, which interacted with out universe only through the weak interactions, and that the decay seen was the decay of the “shadow \(\ce{K2^0}\),” (8) the failure of the exponential decay law, 9) the failure of the principle of superposition in quantum mechanics, and 10) that the decay pions were not bosons.

As one can see, the limits placed on alternatives were not very stringent. By the end of 1967, all of the alternatives had been tested and found wanting, leaving CP symmetry unprotected. Here the differing judgments of the scientific community about what was worth proposing and pursuing led to a wide variety of alternatives being tested.

Hacking’s second sticking point is nominalism, or name-ism. He notes that in its most extreme form nominalism denies that there is anything in common or peculiar to objects selected by a name, such as “Douglas fir” other than that they are called Douglas fir. Opponents contend that good names, or good accounts of nature, tell us something correct about the world. This is related to the realism-antirealism debate concerning the status of unobservable entities that has plagued philosophers for millennia. For example Bas van Fraassen (1980), an antirealist, holds that we have no grounds for belief in unobservable entities such as the electron and that accepting theories about the electron means only that we believe that the things the theory says about observables is true. A realist claims that electrons really exist and that as, for example, Wilfred Sellars remarked, “to have good reason for holding a theory is ipso facto to have good reason for holding that the entities postulated by the theory exist (Sellars 1962, p. 97).” In Hacking’s view a scientific nominalist is more radical than an antirealist and is just as skeptical about fir trees as they are about electrons. A nominalist further believes that the structures we conceive of are properties of our representations of the world and not of the world itself. Hacking refers to opponents of that view as inherent structuralists.

Hacking also remarks that this point is related to the question of “scientific facts.” Thus, constructivists Latour and Woolgar originally entitled their book Laboratory Life: The Social Construction of Scientific Facts (1979). Andrew Pickering entitled his history of the quark model Constructing Quarks (Pickering 1984a). Physicists argue that this demeans their work. Steven Weinberg, a realist and a physicist, criticized Pickering’s title by noting that no mountaineer would ever name a book Constructing Everest . For Weinberg, quarks and Mount Everest have the same ontological status. They are both facts about the world. Hacking argues that constructivists do not, despite appearances, believe that facts do not exist, or that there is no such thing as reality. He cites Latour and Woolgar “that ‘out-there-ness’ is a consequence of scientific work rather than its cause (Latour and Woolgar 1986, p. 180).” Hacking reasonably concludes that,

Latour and Woolgar were surely right. We should not explain why some people believe that \(p\) by saying that \(p\) is true, or corresponds to a fact, or the facts. For example: someone believes that the universe began with what for brevity we call a big bang. A host of reasons now supports this belief. But after you have listed all the reasons, you should not add, as if it were an additional reason for believing in the big bang, ‘and it is true that the universe began with a big bang.’ Or ‘and it is a fact.’This observation has nothing peculiarly to do with social construction. It could equally have been advanced by an old-fashioned philosopher of language. It is a remark about the grammar of the verb ‘to explain’ (Hacking 1999, pp. 80–81).

One might add, however, that the reasons Hacking cites as supporting that belief are given to us by valid experimental evidence and not by the social and personal interests of scientists. Latour and Woolgar might not agree. Franklin argues that we have good reasons to believe in facts, and in the entities involved in our theories, always remembering, of course, that science is fallible.

Hacking’s third sticking point is the external explanations of stability.

The constructionist holds that explanations for the stability of scientific belief involve, at least in part, elements that are external to the content of science. These elements typically include social factors, interests, networks, or however they be described. Opponents hold that whatever be the context of discovery, the explanation of stability is internal to the science itself (Hacking 1999, p. 92). Rationalists think that most science proceeds as it does in the light of good reasons produced by research. Some bodies of knowledge become stable because of the wealth of good theoretical and experimental reasons that can be adduced for them. Constructivists think that the reasons are not decisive for the course of science. Nelson (1994) concludes that this issue will never be decided. Rationalists, at least retrospectively, can always adduce reasons that satisfy them. Constructivists, with equal ingenuity, can always find to their own satisfaction an openness where the upshot of research is settled by something other than reason. Something external. That is one way of saying we have found an irresoluble “sticking point” (pp. 91–92)

Thus, there is a rather severe disagreement on the reasons for the acceptance of experimental results. For some, like Staley, Galison and Franklin, it is because of epistemological arguments. For others, like Pickering, the reasons are utility for future practice and agreement with existing theoretical commitments. Although the history of science shows that the overthrow of a well-accepted theory leads to an enormous amount of theoretical and experimental work, proponents of this view seem to accept it as unproblematical that it is always agreement with existing theory that has more future utility. Hacking and Pickering also suggest that experimental results are accepted on the basis of the mutual adjustment of elements which includes the theory of the phenomenon.

Nevertheless, everyone seems to agree that a consensus does arise on experimental results.

We have encountered (Section 2.2.1) Franklin’s definition of calibration as the use of a surrogate signal that has been established independently of the apparatus being calibrated, or as the reproduction of independently (on an independent apparatus) well established phenomena. He used this account in order to argue against Collins’s view of the viciousness of the experimenter’s regress. Yet other accounts of calibration explaining the process of measurement evoke similar worries.

E. Tal defines calibration as “the activity of establishing a correlation between indications of a measuring instrument and quantity values associated with a measurement standard” (Tal 2017a, 243). Mere “instrument indications” (Tal 2017a) become “measurement outcomes” when interpreted in a larger context of theoretical background and calibration of the measuring instrument. Yet Boyd (2021, 43) worries that this leaves the measurement open to the vicious regress akin to the experimenter’s regress H. Collins has argued for (see Section 2.2.1), since the experimenters do not “have access to true values of the measurand [i.e., whatever is measured] to use as standards of calibration”.

The regress may be blocked by comparisons across measuring outcomes performed in differently modeled measurements, i.e. across different measurement contexts (the context being constituted by intricacies of the operational and theoretical background of apparata). (Tal 2017a, 239) These different contexts are idealized and then compared in order to establish whether they cohere to the predictions concerning the measurand. Thus, calibration is “the activity of modeling different processes and testing the consequences of such models for mutual compatibility” (Tal 2017a, 246). An act of calibration is effectively the comparison of idealized models across measuring processes, where an act of measurement becomes justified by the models’ mutual coherence.

Boyd (2021, 46) states that idealizations of measurement processes cannot provide the foundation for objective measurement since idealizations strip the context of its key details. In fact, the measurement has an epistemic utility by virtue of the context-sensitive details. The measurement outcomes gain epistemic value only as “enriched evidence”, i.e., in the wider empirical and theoretical context.

Moreover, Tal’s view conflates prediction and calibration (Boyd 2021, 48) taking us back to the viciousness of the experimenter’s regress. Thus, “Calibration was supposed to disrupt the regress because an instrument could be judged to be working correctly in virtue of something other than success at its principal aim” (Boyd 2021, 48). Calibration has to provide a point outside the web of cohering predictions if it is to break the regress, e.g. by the subtle processing of instrument indications.

Progressive coherentism’s attempted way out of the vicious regress, as proposed by Hasok Chang (2004, 2007), points to a historical trajectory of the development of measuring procedures and calibration. The web of cohering measuring process is dynamic: it unfolds in time as a spiral. And the measurement process that initially aims at prediction eventually becomes a result used for calibrating processes. Yet, such spiraling progress may be just an elaborate dynamic coherence web of Collins’ type where inter-subjective agreement is the foundation, if other non-empirical virtues such as “creative achievement” play as important role as the empirical ones as claimed by Chang (2007). If so, calibration does not offer an independent leverage to the measurement outcomes after all.

Perović (2017) analyzes in-situ calibration procedures in the Large Hadron Collider pointing out that calibration serves as an epistemic leverage of Franklin’s type during the commissioning phase of the experimental apparatus. But various calibration procedures continue all along and gradually feed into the measurement itself during the entire measurement process. Boyd (2021) further explores commissioning procedures and the gradual transition from “engineering data“ to “science data”, where criteria of prediction significantly change. The apparatus initially relies on the existing well-known data but then coherence with other apparata that Tal insists on becomes increasingly less significant in justifying the measurement outcomes.

Authors like Thomas Kuhn and Paul Feyerabend put forward the view that evidence does not confirm or refute a scientific theory since it is laden by it. Evidence is not a set of observational sentences autonomous from theoretical ones, as logical positivists believed. Each new theory or a theoretical paradigm, as Kuhn labeled larger theoretical frameworks, produces, as it were, evidence anew.

Thus, theoretical concepts infect the entire experimental process from the stage of design and preparation to the production and analysis of data. A simple example that is supposed to convincingly illustrate this view are measurements of temperature with a mercury thermometer one uses in order to test whether objects expand when their temperature increases. Note that in such a case one tests the hypothesis by relying on the very assumption that the expansion of mercury indicates increase in temperature.

There may be a fairly simple way out of the vicious circle in which theory and experiment are caught in this particular case of theory-ladenness. It may suffice to calibrate the mercury thermometer with a constant volume gas thermometer, for example, where its use does not rely on the tested hypothesis but on the proportionality of the pressure of the gas and its absolute temperature (Franklin et al. 1989).

Although most experiments are far more complex than this toy example, one could certainly approach the view that experimental results are theory-laden on a case-by-case basis. Yet there may be a more general problem with the view.

Bogen and Woodward (1988) argued that debate on the relationship between theory and observation overlooks a key ingredient in the production of experimental evidence, namely the experimental phenomena. The experimentalists distill experimental phenomena from raw experimental data (e.g. electronic or digital tracks in particle colliders) using various tools of statistical analysis. Thus, identification of an experimental phenomenon as significant (e.g. a peak at a particular energy of colliding beams) is free of the theory that the experiment may be designed to test (e.g. the prediction of a particular particle). Only when significant phenomenon has been identified can a stage of data analysis begin in which the phenomenon is deemed to either support or refute a theory. Thus, the theory-ladenness of evidence thesis fails at least in some experiments in physics.

The authors substantiate their argument in part through an analysis of experiments that led to a breakthrough discovery of weak neutral currents. It is a type of force produced by so-called bosons — short-lived particles responsible for energy transfer between other particles such as hadrons and leptons. The relevant peaks were recognized as significant via statistical analysis of data, and later on interpreted as evidence for the existence of the bosons.

This view and the case study have been challenged by Schindler (2011). He argues that the tested theory was critical in the assessment of the reliability of data in the experiments with weak neutral currents. He also points out that, on occasion, experimental data can even be ignored if they are deemed irrelevant from a theoretical perspective that physicists find particularly compelling. This was the case in experiments with so-called zebra pattern magnetic anomalies on the ocean floor. The readings of new apparatuses used to scan the ocean floor produced intriguing signals. Yet the researchers could not interpret these signals meaningfully or satisfyingly distinguish them from noise unless they relied on some theoretical account of both the structure of the ocean floor and the earth’s magnetic field.

Karaca (2013) points out that a crude theory-observation distinction is particularly unhelpful in understanding high-energy physics experiments. It fails to capture the complexity of relevant theoretical structures and their relation to experimental data. Theoretical structures can be composed of background, model, and phenomenological theories. Background theories are very general theories (e.g. quantum field theory or quantum electrodynamics) that define the general properties of physical particles and their interactions. Models are specific instances of background theories that define particular particles and their properties. While phenomenological theories develop testable predictions based on these models.

Now, each of these theoretical segments stands in a different relationship to experimental data—the experiments can be laden by a different segment to a different extent. This requires a nuanced categorization of theory-ladeness, from weak to strong.

Thus, an experimental apparatus can be designed to test a very specific theoretical model. UA1 and UA2 detectors at CERN’s Super Proton Synchrotron were designed to detect particles only in a very specific energy regime in which W and Z bosons of the Standard Model were expected to exist.

In contrast, exploratory experiments approach phenomena without relying on a particular theoretical model. Thus, sometimes a theoretical framework for an experiment consists of phenomenological theory alone. Karaca argues that experiments with deep-inelastic electron-proton scattering in the late 1960s and early 1970s are example of such weakly theory-laden experiments. The application of merely phenomenological parameters in the experiment resulted in the very important discovery of the composite rather than point-like structure of hadrons (protons and neutrons), or the so-called scaling law. And this eventually led to a successful theoretical model of the composition of hadrons, namely quantum chromodynamics, or the quark-model of strong interactions.

3. The Roles of Experiment

Although experiment often takes its importance from its relation to theory, Hacking pointed out that it often has a life of its own, independent of theory. He notes the pristine observations of Carolyn Herschel’s discovery of comets, William Herschel’s work on “radiant heat,” and Davy’s observation of the gas emitted by algae and the flaring of a taper in that gas. In none of these cases did the experimenter have any theory of the phenomenon under investigation. One may also note the nineteenth century measurements of atomic spectra and the work on the masses and properties on elementary particles during the 1960s. Both of these sequences were conducted without any guidance from theory.

In deciding what experimental investigation to pursue, scientists may very well be influenced by the equipment available and their own ability to use that equipment (McKinney 1992). Thus, when the Mann-O’Neill collaboration was doing high energy physics experiments at the Princeton-Pennsylvania Accelerator during the late 1960s, the sequence of experiments was (1) measurement of the \(\ce{K+}\) decay rates, (2) measurement of the \(\ce{K+_{e 3}}\) branching ratio and decay spectrum, (3) measurement of the \(\ce{K+_{e 2}}\) branching ratio, and (4) measurement of the form factor in \(\ce{K+_{e 3}}\) decay. These experiments were performed with basically the same experimental apparatus, but with relatively minor modifications for each particular experiment. By the end of the sequence the experimenters had become quite expert in the use of the apparatus and knowledgeable about the backgrounds and experimental problems. This allowed the group to successfully perform the technically more difficult experiments later in the sequence. We might refer to this as “instrumental loyalty” and the “recycling of expertise” (Franklin 1997b). This meshes nicely with Galison’s view of experimental traditions. Scientists, both theorists and experimentalists, tend to pursue experiments and problems in which their training and expertise can be used.

Hacking also remarks on the “noteworthy observations” on Iceland Spar by Bartholin, on diffraction by Hooke and Grimaldi, and on the dispersion of light by Newton. “Now of course Bartholin, Grimaldi, Hooke, and Newton were not mindless empiricists without an ‘idea’ in their heads. They saw what they saw because they were curious, inquisitive, reflective people. They were attempting to form theories. But in all these cases it is clear that the observations preceded any formulation of theory” (Hacking 1983, p. 156). In all of these cases we may say that these were observations waiting for, or perhaps even calling for, a theory. The discovery of any unexpected phenomenon calls for a theoretical explanation.

3.2 Confirmation and Refutation

Nevertheless several of the important roles of experiment involve its relation to theory. Experiment may confirm a theory, refute a theory, or give hints to the mathematical structure of a theory.

Let us consider first an episode in which the relation between theory and experiment was clear and straightforward. This was a “crucial” experiment, one that decided unequivocally between two competing theories, or classes of theory. The episode was that of the discovery that parity, mirror-reflection symmetry or left-right symmetry, is not conserved in the weak interactions. (For details of this episode see Franklin (1986, Ch. 1) and Appendix 1 ). Experiments showed that in the beta decay of nuclei the number of electrons emitted in the same direction as the nuclear spin was different from the number emitted opposite to the spin direction. This was a clear demonstration of parity violation in the weak interactions.

After the discovery of parity and charge conjugation nonconservation, and following a suggestion by Landau, physicists considered CP (combined parity and particle-antiparticle symmetry), which was still conserved in the experiments, as the appropriate symmetry. One consequence of this scheme, if CP were conserved, was that the \(\ce{K1^0}\) meson could decay into two pions, whereas the \(\ce{K2^0}\) meson could not. [ 10 ] Thus, observation of the decay of \(\ce{K2^0}\) into two pions would indicate CP violation. The decay was observed by a group at Princeton University. Although several alternative explanations were offered, experiments eliminated each of the alternatives leaving only CP violation as an explanation of the experimental result. (For details of this episode see Franklin (1986, Ch. 3) and Appendix 2 .)

In both of the episodes discussed previously, those of parity nonconservation and of CP violation, we saw a decision between two competing classes of theories. This episode, the discovery of Bose-Einstein condensation (BEC), illustrates the confirmation of a specific theoretical prediction 70 years after the theoretical prediction was first made. Bose (1924) and Einstein (1924; 1925) predicted that a gas of noninteracting bosonic atoms will, below a certain temperature, suddenly develop a macroscopic population in the lowest energy quantum state. [ 11 ] (For details of this episode see Appendix 3 .)

3.3 Complications

In the three episodes discussed in the previous section, the relation between experiment and theory was clear. The experiments gave unequivocal results and there was no ambiguity about what theory was predicting. None of the conclusions reached has since been questioned. Parity and CP symmetry are violated in the weak interactions and Bose-Einstein condensation is an accepted phenomenon. In the practice of science things are often more complex. Experimental results may be in conflict, or may even be incorrect. Theoretical calculations may also be in error or a correct theory may be incorrectly applied. There are even cases in which both experiment and theory are wrong. As noted earlier, science is fallible. In this section I will discuss several episodes which illustrate these complexities.

The episode of the fifth force is the case of a refutation of an hypothesis, but only after a disagreement between experimental results was resolved. The “Fifth Force” was a proposed modification of Newton’s Law of Universal Gravitation. The initial experiments gave conflicting results: one supported the existence of the Fifth Force whereas the other argued against it. After numerous repetitions of the experiment, the discord was resolved and a consensus reached that the Fifth Force did not exist. (For details of this episode see Appendix 4 .)

The Stern-Gerlach experiment was regarded as crucial at the time it was performed, but, in fact, wasn’t. [ 12 ] In the view of the physics community it decided the issue between two theories, refuting one and supporting the other. In the light of later work, however, the refutation stood, but the confirmation was questionable. In fact, the experimental result posed problems for the theory it had seemingly confirmed. A new theory was proposed and although the Stern-Gerlach result initially also posed problems for the new theory, after a modification of that new theory, the result confirmed it. In a sense, it was crucial after all. It just took some time.

The Stern-Gerlach experiment provides evidence for the existence of electron spin. These experimental results were first published in 1922, although the idea of electron spin wasn’t proposed by Goudsmit and Uhlenbeck until 1925 (1925; 1926). One might say that electron spin was discovered before it was invented. (For details of this episode see Appendix 5 ).

In the last section we saw some of the difficulty inherent in experiment-theory comparison. One is sometimes faced with the question of whether the experimental apparatus satisfies the conditions required by theory, or conversely, whether the appropriate theory is being compared to the experimental result. A case in point is the history of experiments on the double-scattering of electrons by heavy nuclei (Mott scattering) during the 1930s and the relation of these results to Dirac’s theory of the electron, an episode in which the question of whether or not the experiment satisfied the conditions of the theoretical calculation was central. Initially, experiments disagreed with Mott’s calculation, casting doubt on the underlying Dirac theory. After more than a decade of work, both experimental and theoretical, it was realized that there was a background effect in the experiments that masked the predicted effect. When the background was eliminated experiment and theory agreed. ( Appendix 6 )

Ever vaster amounts of data have been produced by particle colliders as they have grown from room-size apparata, to tens of kilometers long mega-labs. Vast numbers of background interactions that are well understood and theoretically uninteresting occur in the detector. These have to be combed in order to identify interactions of potential interest. This is especially true of hadron (proton-proton) colliders like the Large Hadron Collider (LHC), where the Higgs boson was discovered. Protons that collide in the LHC and similar hadron colliders are composed of more elementary particles, collectively labeled partons. Partons mutually interact, exponentially increasing the number of background interactions. In fact, a minuscule number of interactions are selected from the overwhelming number that occur in the detector. (In contrast, lepton collisions, such as collisions of electrons and positrons, produce much lower backgrounds, since leptons are not composed of more elementary particles.)

Thus, a successful search for new elementary particles critically depends on successfully crafting selection criteria and techniques at the stage of data collection and at the stage of data analysis. But gradual development and changes in data selection procedures in the colliders raises an important epistemological concern. The main reason for this concern is nicely anticipated by the following question, which was posed by one of the most prominent experimentalists in particle physics: “What is the extent to which we are negating the discovery potential of very-high-energy proton machines by the necessity of rejecting, a priori, the events we cannot afford to record?” (Panofsky 1994, 133). In other words, how does one decide which interactions to detect and analyze in a multitude, in order to minimize the possibility of throwing out novel and unexplored ones?

One way of searching through vast amounts of data that are already in, i.e. those that the detector has already delivered, is to look for occurrences that remain robust under varying conditions of detection. Physicists employ the technique of data cuts in such analysis. They cut out data that may be unreliable—when, for instance, a data set may be an artefact rather than a genuine particle interaction the experimenters expect. E.g. a colliding beam may interact with the walls of the detector and not with the other colliding beam, while producing a signal identical to the signal the experimenters expected the beam-beam interaction to produce. Thus, if under various data cuts a result remains stable, then it is increasingly likely to be correct and to represent the genuine phenomenon the physicists think it represents. The robustness of the result under various data cuts minimizes the possibility that the detected phenomenon only mimics the genuine one (Franklin 2013, 224–5).

At the data-acquisition stage, however, this strategy does not seem applicable. As Panofsky suggests, one does not know with certainty which of the vast number of the events in the detector may be of interest.

Yet, Karaca (2011) [ 13 ] argues that a form of robustness is in play even at the acquisition stage. This experimental approach amalgamates theoretical expectations and empirical results, as the example of the hypothesis of specific heavy particles is supposed to illustrate.

Along with the Standard Model of particle physics, a number of alternative models have been proposed. Their predictions of how elementary particles should behave often differ substantially. Yet in contrast to the Standard Model, they all share the hypothesis that there exist heavy particles that decay into particles with high transverse momentum.

Physicists apply a robustness analysis in testing this hypothesis, the argument goes. First, they check whether the apparatus can detect known particles similar to those predicted. Second, guided by the hypothesis, they establish various trigger algorithms. (The trigger algorithms, or “the triggers”, determine at what exact point in time and under which conditions a detector should record interactions. They are necessary because the frequency and the number of interactions far exceed the limited recording capacity.) And, finally, they observe whether any results remain stable across the triggers.

Yet even in this theoretical-empirical form of robustness, as Franklin (2013, 225) points out, “there is an underlying assumption that any new physics will resemble known physics”—usually a theory of the day. And one way around this problem is for physicists to produce as many alternative models as possible, including those that may even seem implausible at the time.

Perovic (2011) suggests that such a potential failure, namely to spot potentially relevant events occurring in the detector, may be also a consequence of the gradual automation of the detection process.

The early days of experimentation in particle physics, around WWII, saw the direct involvement of the experimenters in the process. Experimental particle physics was a decentralized discipline where experimenters running individual labs had full control over the triggers and analysis. The experimenters could also control the goals and the design of experiments. Fixed target accelerators, where the beam hits the detector instead of another beam, produced a number of particle interactions that was manageable for such labs. The chance of missing an anomalous event not predicted by the current theory was not a major concern in such an environment.

Yet such labs could process a comparatively small amount of data. This has gradually become an obstacle, with the advent of hadron colliders. They work at ever-higher energies and produce an ever-vaster number of background interactions. That is why the experimental process has become increasingly automated and much more indirect. Trained technicians instead of experimenters themselves at some point started to scan the recordings. Eventually, these human scanners were replaced by computers, and a full automation of detection in hadron colliders has enabled the processing of vast number of interactions. This was the first significant change in the transition from small individual labs to mega-labs.

The second significant change concerned the organization and goals of the labs. The mega-detectors and the amounts of data they produced required exponentially more staff and scientists. This in turn led to even more centralized and hierarchical labs and even longer periods of design and performance of the experiments. As a result, focusing on confirming existing dominant hypotheses rather than on exploratory particle searches was the least risky way of achieving results that would justify unprecedented investments.

Now, an indirect detection process combined with mostly confirmatory goals is conducive to overlooking of unexpected interactions. As such, it may impede potentially crucial theoretical advances stemming from missed interactions.

This possibility that physicists such as Panofsky have acknowledged is not a mere speculation. In fact, the use of semi-automated, rather than fully-automated regimes of detection turned out to be essential for a number of surprising discoveries that led to theoretical breakthroughs.

Perovic (2011) analyzes several such cases, most notably the discovery of the J/psi particle that provided the first substantial piece of evidence for the existence of the charmed quark. In the experiments, physicists were able to perform exploratory detection and visual analysis of practically individual interactions due to low number of background interactions in the linear electron-positron collider. And they could afford to do this in an energy range that the existing theory did not recognize as significant, which led to them making the discovery. None of this could have been done in the fully automated detecting regime of hadron colliders that are indispensable when dealing with an environment that contains huge numbers of background interactions.

And in some cases, such as the Fermilab experiments that aimed to discover weak neutral currents, an automated and confirmatory regime of data analysis contributed to the failure to detect particles that were readily produced in the apparatus.

The complexity of the discovery process in particle physics does not end with concerns about what exact data should be chosen out of the sea of interactions. The so-called look-elsewhere effect results in a tantalizing dilemma at the stage of data analysis.

Suppose that our theory tells us that we will find a particle in an energy range. And suppose we find a significant signal in a section of that very range. Perhaps we should keep looking elsewhere within the range to make sure it is not another particle altogether we have discovered. It may be a particle that left other undetected traces in the range that our theory does not predict, along with the trace we found. The question is to what extent we should look elsewhere before we reach a satisfying level of certainty that it is the predicted particle we have discovered.

Physicists faced such a dilemma during the search for the Higgs boson at the Large Hadron Collider at CERN (Dawid 2015).

The Higgs boson is a particle responsible for the mass of other particles. It is a scalar field that “pulls back” moving and interacting particles. This pull, which we call mass, is different for different particles. It is predicted by the Standard Model, whereas alternative models predict somewhat similar Higgs-like particles.

A prediction based on the Standard Model tells us with high probability that we will find the Higgs particle in a particular range. Yet a simple and an inevitable fact of finding it in a particular section of that range may prompt us to doubt whether we have truly found the exact particle our theory predicted. Our initial excitement may vanish when we realize that we are much more likely to find a particle of any sort—not just the predicted particle—within the entire range than in a particular section of that range. Thus, the probability of finding the Higgs anywhere within a given energy range (consisting of eighty energy ‘bins’) is much higher than the probability of finding it at a particular energy scale within that range (i.e. in any individual bin). In fact, the likelihood of us finding it in a particular bin of the range is about hundred times lower.

In other words, the fact that we will inevitably find the particle in a particular bin, not only in a particular range, decreases the certainty that it was the Higgs we found. Given this fact alone we should keep looking elsewhere for other possible traces in the range once we find a significant signal in a bin. We should not proclaim the discovery of a particle predicted by the Standard Model (or any model for that matter) too soon. But for how long should we keep looking elsewhere? And what level of certainty do we need to achieve before we proclaim discovery?

The answer boils down to the weight one gives the theory and its predictions. This is the reason the experimentalists and theoreticians had divergent views on the criterion for determining the precise point at which they could justifiably state ‘Our data indicate that we have discovered the Higgs boson’. Theoreticians were confident that a finding within the range (any of eighty bins) that was of standard reliability (of three or four sigma), coupled with the theoretical expectations that Higgs would be found, would be sufficient. In contrast, experimentalists argued that at no point of data analysis should the pertinence of the look-elsewhere effect be reduced, and the search proclaimed successful, with the help of the theoretical expectations concerning Higgs. One needs to be as careful in combing the range as one practically may. As a result, the experimentalists’ preferred value of sigmas for announcing the discovery was five. This is a standard under which very few findings have turned out to be a fluctuation in the past.

Dawid argues that a question of an appropriate statistical analysis of data is at the heart of the dispute. The reasoning of the experimentalists relied on a frequentist approach that does not specify the probability of the tested hypothesis. It actually isolates statistical analysis of data from the prior probabilities. The theoreticians, however, relied on Bayesian analysis. It starts with prior probabilities of initial assumptions and ends with the assessment of the probability of tested hypothesis based on the collected evidence. The question remains whether the experimentalists’ reasoning was fully justified. The prior expectations that the theoreticians had included in their analysis had already been empirically corroborated by previous experiments after all.

3.4 Other Roles

Experiment can also provide us with evidence for the existence of the entities involved in our theories. J.J. Thomson’s experiments on cathode rays provided grounds for belief in the existence of electrons. (For details of this episode see Appendix 7 ).

Experiment can also help to articulate a theory. Experiments on beta decay during from the 1930s to the 1950s determined the precise mathematical form of Fermi’s theory of beta decay. (For details of this episode see Appendix 8 .)

The distinction between observation and experiment is relatively little discussed in philosophical literature, despite its continuous relevance to the scientific community and beyond in understanding specific traits and segments of the scientific process and the knowledge it produces.

Daston and her coauthors (Daston 2011; Daston and Lunbeck 2011; Daston and Galison 2007) have convincingly demonstrated that the distinction has played a role in delineating various features of scientific practice. It has helped scientists articulate their reflections on their own practice.

Observation is philosophically a loaded term, yet the epistemic status of scientific observation has evolved gradually with the advance of scientific techniques of inquiry and the scientific communities pursuing them. Daston succinctly summarizes this evolution in the following passage:

Characteristic of the emergent epistemic genre of the observations was, first, an emphasis on singular events, witnessed first hand (autopsia) by a named author (in contrast to the accumulation of anonymous data over centuries described by Cicero and Pliny as typical of observationes); second, a deliberate effort to separate observation from conjecture (in contrast to the medieval Scholastic connection of observation with the conjectural sciences, such as astrology); and third, the creation of virtual communities of observers dispersed over time and space, who communicated and pooled their observations in letters and publications (in contrast to passing them down from father to son or teacher to student as rare and precious treasures). (2011, 81)

Observation gradually became juxtaposed to other, more complex modes of inquiry such as experiment, “whose meaning shifted from the broad and heterogeneous sense of experimentum as recipe, trial, or just common experience to a concertedly artificial manipulation, often using special instruments and designed to probe hidden causes” (Daston 2011, 82).

In the 17th century, observation and experiment were seen as “an inseparable pair” (Daston 2011, 82) and by the 19th century they were understood to be essentially opposed, with the observer increasingly seen as passive and thus epistemically inferior to the experimenter. In fact, already Leibniz anticipated this view stating that “[t]here are certain experiments that would be better called observations, in which one considers rather than produces the work” (Daston 2011, 86). This aspect of the distinction has been a mainstay of understanding scientific practice ever since.

Shapere (1982) pointed out that the usage of the notion of observation is embedded in scientific practice, including testing and justification of scientific theories. Neutrinos arriving from the Sun are said to be observed in the detector but are also said to be an observation of the Sun’s core. Yet, the background knowledge provides the foundation for a meaningful distinction. One the one hand there are weakly interacting neutrinos leaving the Sun’s core and entering detector practically interrupted. This may be justifiably qualified as “direct observation” of the Sun’s core. On the other hand, there is a rather indirect observation of the core via detection of light-photons that travel for billions of years from the Sun’s core through the plasma to the electromagnetic detectors.

There are currently two prominent and opposed views of the experiment-observation distinction. Ian Hacking has characterized it as well-defined, while avoiding the claim that observation and experiment are opposites (Hacking 1983, 173). According to him, the notions signify different things in scientific practice. The experiment is a thorough manipulation that creates a new phenomenon, and observation of the phenomenon is its outcome. If scientists can manipulate a domain of nature to such an extent that they can create a new phenomenon in a lab, a phenomenon that normally cannot be observed in nature, then they have truly observed the phenomenon (Hacking 1989, 1992).

Meanwhile, other authors concur that “the familiar distinction between observation and experiment … [is] an artefact of the disembodied, reconstructed character of retrospective accounts” (Gooding 1992, 68). The distinction “collapses” when we are faced with actual scientific practice as a process, and “Hacking’s observation versus experiment framework does not survive intact when put to the test in a range of cases of scientific experimentation” (Malik 2017, 85). First, the uses of the distinction cannot be compared across scientific fields. And second, as Gooding (1992) suggests, observation is a process too, not simply a static result of manipulation. Thus, both observation and experiment are seen as concurrent processes blended together in scientific practice.

Malik (2017, 86) states that these arguments are the reason why “very few [authors] use Hacking’s nomenclature of observation/experiment” and goes so far to conclude that “to (try to) distinguish between observation and experiment is futile.” There is no point delineating the two except perhaps in certain narrow domains; e.g., Hacking’s notion of the experiment based on creating phenomena might be useful within a narrow domain of particle physics. (See also Chang 2011.) He advocates avoiding the distinction altogether and opting for “the terminology [that] underlines this sense of continuousness” (Malik 2017, 88) instead. If we want to analyze scientific practice, the argument goes, we should leave behind the idea of the distinction as fundamental and turn to the characterization and analysis of various “epistemic activities” instead, e.g., along the lines suggested by Chang (2011).

A rather obvious danger of this approach is an over-emphasis on the continuousness of the notions of observation and experiment that results in inadvertent equivocation. And this, in turn, results in sidelining the distinction and its subtleties in the analysis of the scientific practice, despite their crucial role in articulating and developing that practice since the 17th century. It is possible that these two notions form a continuum spreading along the axes of manipulability and accessibility of both target phenomena and observational conditions, and that the key points on such continuum define various evolving practices. Thus, each scientific practice may be located on the continuum, with the location defining both its epistemic properties and the epistemic (as well as ethical) obligations that underlie it at a given time. (Perović 2021)

On the one hand, the extent of the manipulation of phenomena and research conditions ranges from bodily manipulations (e.g. finding a convenient location to observe a planet with the naked eye) to the production of new phenomena in an elaborate apparatus like LHC at CERN, where the label “experiment” aims to delineate a substantial threshold of manipulation. On the other hand, depending on the background knowledge and aims of the research, observational accessibility ranges from mere detection of a potentially interesting phenomenon (e.g. a metal detector detects something in the ground), all the way to direct observation of the phenomenon’s properties (e.g., the microscopic analysis of a metal object).

5. Some Comparisons With Experiment in Biology

One comment that has been made concerning the philosophy of experiment is that all of the examples are taken from physics and are therefore limited. In this section arguments will be presented that these discussions also apply to biology.

Although all of the illustrations of the epistemology of experiment come from physics, David Rudge (1998; 2001) has shown that they are also used in biology. His example is Kettlewell’s (1955; 1956; 1958) evolutionary biology experiments on the Peppered Moth, Biston betularia . The typical form of the moth has a pale speckled appearance and there are two darker forms, f. carbonaria , which is nearly black, and f. insularia , which is intermediate in color. The typical form of the moth was most prevalent in the British Isles and Europe until the middle of the nineteenth century. At that time things began to change. Increasing industrial pollution had both darkened the surfaces of trees and rocks and had also killed the lichen cover of the forests downwind of pollution sources. Coincident with these changes, naturalists had found that rare, darker forms of several moth species, in particular the Peppered Moth, had become common in areas downwind of pollution sources.

Kettlewell attempted to test a selectionist explanation of this phenomenon. E.B. Ford (1937; 1940) had suggested a two-part explanation of this effect: 1) darker moths had a superior physiology and 2) the spread of the melanic gene was confined to industrial areas because the darker color made carbonaria more conspicuous to avian predators in rural areas and less conspicuous in polluted areas. Kettlewell believed that Ford had established the superior viability of darker moths and he wanted to test the hypothesis that the darker form of the moth was less conspicuous to predators in industrial areas.

Kettlewell’s investigations consisted of three parts. In the first part he used human observers to investigate whether his proposed scoring method would be accurate in assessing the relative conspicuousness of different types of moths against different backgrounds. The tests showed that moths on “correct” backgrounds, typical on lichen covered backgrounds and dark moths on soot-blackened backgrounds were almost always judged inconspicuous, whereas moths on “incorrect” backgrounds were judged conspicuous.

The second step involved releasing birds into a cage containing all three types of moth and both soot-blackened and lichen covered pieces of bark as resting places. After some difficulties (see Rudge 1998 for details), Kettlewell found that birds prey on moths in an order of conspicuousness similar to that gauged by human observers.

The third step was to investigate whether birds preferentially prey on conspicuous moths in the wild. Kettlewell used a mark-release-recapture experiment in both a polluted environment (Birmingham) and later in an unpolluted wood. He released 630 marked male moths of all three types in an area near Birmingham, which contained predators and natural boundaries. He then recaptured the moths using two different types of trap, each containing virgin females of all three types to guard against the possibility of pheromone differences.

Kettlewell found that carbonaria was twice as likely to survive in soot-darkened environments (27.5 percent) as was typical (12.7 percent). He worried, however, that his results might be an artifact of his experimental procedures. Perhaps the traps used were more attractive to one type of moth, that one form of moth was more likely to migrate, or that one type of moth just lived longer. He eliminated the first alternative by showing that the recapture rates were the same for both types of trap. The use of natural boundaries and traps placed beyond those boundaries eliminated the second, and previous experiments had shown no differences in longevity. Further experiments in polluted environments confirmed that carbonaria was twice as likely to survive as typical. An experiment in an unpolluted environment showed that typical was three times as likely to survive as carbonaria . Kettlewell concluded that such selection was the cause of the prevalence of carbonaria in polluted environments.

Rudge also demonstrates that the strategies used by Kettlewell are those described above in the epistemology of experiment. His examples are given in Table 1. (For more details see Rudge 1998).

Table 1. Examples of epistemological strategies used by experimentalists in evolutionary biology, from H.B.D. Kettlewell’s (1955, 1956, 1958) investigations of industrial melanism. (See Rudge 1998).

The roles that experiment plays in physics are also those it plays in biology. In the previous section we have seen that Kettlewell’s experiments both test and confirm a theory. I discussed earlier a set of crucial experiments that decided between two competing classes of theories, those that conserved parity and those that did not. In this section I will discuss an experiment that decided among three competing mechanisms for the replication of DNA, the molecule now believed to be responsible for heredity. This is another crucial experiment. It strongly supported one proposed mechanism and argued against the other two. (For details of this episode see Holmes 2001.)

In 1953 Francis Crick and James Watson proposed a three-dimensional structure for deoxyribonucleic acid (DNA) (Watson and Crick 1953a). Their proposed structure consisted of two polynucleotide chains helically wound about a common axis. This was the famous “Double Helix”. The chains were bound together by combinations of four nitrogen bases — adenine, thymine, cytosine, and guanine. Because of structural requirements only the base pairs adenine-thymine and cytosine-guanine are allowed. Each chain is thus complementary to the other. If there is an adenine base at a location in one chain there is a thymine base at the same location on the other chain, and vice versa. The same applies to cytosine and guanine. The order of the bases along a chain is not, however, restricted in any way, and it is the precise sequence of bases that carries the genetic information.

The significance of the proposed structure was not lost on Watson and Crick when they made their suggestion. They remarked, “It has not escaped our notice that the specific pairing we have postulated immediately suggests a possible copying mechanism for the genetic material.”

If DNA was to play this crucial role in genetics, then there must be a mechanism for the replication of the molecule. Within a short period of time following the Watson-Crick suggestion, three different mechanisms for the replication of the DNA molecule were proposed (Delbruck and Stent 1957). These are illustrated in Figure A. The first, proposed by Gunther Stent and known as conservative replication, suggested that each of the two strands of the parent DNA molecule is replicated in new material. This yields a first generation which consists of the original parent DNA molecule and one newly-synthesized DNA molecule. The second generation will consist of the parental DNA and three new DNAs.

Figure A: Possible mechanisms for DNA replication. (Left) Conservative replication. Each of the two strands of the parent DNA is replicated to yield the unchanged parent DNA and one newly synthesized DNA. The second generation consists of one parent DNA and three new DNAs. (Center) Semiconservative replication. Each first generation DNA molecule contains one strand of the parent DNA and one newly synthesized strand. The second generation consists of two hybrid DNAs and two new DNAs. (Right) Dispersive replication. The parent chains break at intervals, and the parental segments combine with new segments to form the daughter chains. The darker segments are parental DNA and the lighter segments are newly synthesized DNA. From Lehninger (1975).

The second proposed mechanism, known as semiconservative replication is when each strand of the parental DNA acts as a template for a second newly-synthesized complementary strand, which then combines with the original strand to form a DNA molecule. This was proposed by Watson and Crick (1953b). The first generation consists of two hybrid molecules, each of which contains one strand of parental DNA and one newly synthesized strand. The second generation consists of two hybrid molecules and two totally new DNAs. The third mechanism, proposed by Max Delbruck, was dispersive replication, in which the parental DNA chains break at intervals and the parental segments combine with new segments to form the daughter strands.

In this section the experiment performed by Matthew Meselson and Franklin Stahl, which has been called “the most beautiful experiment in biology”, and which was designed to answer the question of the correct DNA replication mechanism will be discussed (Meselson and Stahl 1958). Meselson and Stahl described their proposed method. “We anticipated that a label which imparts to the DNA molecule an increased density might permit an analysis of this distribution by sedimentation techniques. To this end a method was developed for the detection of small density differences among macromolecules. By use of this method, we have observed the distribution of the heavy nitrogen isotope \(\ce{^{15}N}\) among molecules of DNA following the transfer of a uniformly \(\ce{^{15}N}\)-labeled, exponentially growing bacterial population to a growth medium containing the ordinary nitrogen isotope \(\ce{^{14}N}\)” (Meselson and Stahl 1958, pp. 671–672).

Figure B: Schematic representation of the Meselson-Stahl experiment. From Watson (1965).

The experiment is described schematically in Figure B. Meselson and Stahl placed a sample of DNA in a solution of cesium chloride. As the sample is rotated at high speed the denser material travels further away from the axis of rotation than does the less dense material. This results in a solution of cesium chloride that has increasing density as one goes further away from the axis of rotation. The DNA reaches equilibrium at the position where its density equals that of the solution. Meselson and Stahl grew E. coli bacteria in a medium that contained ammonium chloride \((\ce{NH4Cl})\) as the sole source of nitrogen. They did this for media that contained either \(\ce{^{14}N}\), ordinary nitrogen, or \(\ce{^{15}N}\), a heavier isotope. By destroying the cell membranes they could obtain samples of DNA which contained either \(\ce{^{14}N}\) or \(\ce{^{15}N}\). They first showed that they could indeed separate the two different mass molecules of DNA by centrifugation (Figure C). The separation of the two types of DNA is clear in both the photograph obtained by absorbing ultraviolet light and in the graph showing the intensity of the signal, obtained with a densitometer. In addition, the separation between the two peaks suggested that they would be able to distinguish an intermediate band composed of hybrid DNA from the heavy and light bands. These early results argued both that the experimental apparatus was working properly and that all of the results obtained were correct. It is difficult to imagine either an apparatus malfunction or a source of experimental background that could reproduce those results. This is similar, although certainly not identical, to Galileo’s observation of the moons of Jupiter or to Millikan’s measurement of the charge of the electron. In both of those episodes it was the results themselves that argued for their correctness.

Figure C: The separation of \(\ce{^{14}N}\) DNA from \(\ce{^{15}N}\) DNA by centrifugation. The band on the left is \(\ce{^{14}N}\) DNA and that on the right is from \(\ce{^{15}N}\) DNA. From Meselson and Stahl (1958).

Meselson and Stahl then produced a sample of E coli bacteria containing only \(\ce{^{15}N}\) by growing it in a medium containing only ammonium chloride with \(\ce{^{15}N}\) \((\ce{^{15}NH4Cl})\) for fourteen generations. They then abruptly changed the medium to \(\ce{^{14}N}\) by adding a tenfold excess of \(\ce{^{14}NH_4Cl}\). Samples were taken just before the addition of \(\ce{^{14}N}\) and at intervals afterward for several generations. The cell membranes were broken to release the DNA into the solution and the samples were centrifuged and ultraviolet absorption photographs taken. In addition, the photographs were scanned with a recording densitometer. The results are shown in Figure D, showing both the photographs and the densitometer traces. The figure shows that one starts only with heavy (fully-labeled) DNA. As time proceeds one sees more and more half-labeled DNA, until at one generation time only half-labeled DNA is present. “Subsequently only half labeled DNA and completely unlabeled DNA are found. When two generation times have elapsed after the addition of \(\ce{^{14}N}\) half-labeled and unlabeled DNA are present in equal amounts” (p. 676). (This is exactly what the semiconservative replication mechanism predicts). By four generations the sample consists almost entirely of unlabeled DNA. A test of the conclusion that the DNA in the intermediate density band was half labeled was provided by examination of a sample containing equal amounts of generations 0 and 1.9. If the semiconservative mechanism is correct then Generation 1.9 should have approximately equal amounts of unlabeled and half-labeled DNA, whereas Generation 0 contains only fully-labeled DNA. As one can see, there are three clear density bands and Meselson and Stahl found that the intermediate band was centered at \((50 \pm 2)\) percent of the difference between the \(\ce{^{14}N}\) and \(\ce{^{15}N}\) bands, shown in the bottom photograph (Generations 0 and 4.1). This is precisely what one would expect if that DNA were half labeled.

Figure D: (Left) Ultraviolet absorption photographs showing DNA bands from centrifugation of DNA from E. Coli sampled at various times after the addition of an excess of \(\ce{^{14}N}\) substrates to a growing \(\ce{^{15}N}\) culture. (Right) Densitometer traces of the photographs. The initial sample is all heavy (\(\ce{^{15}N}\) DNA). As time proceeds a second intermediate band begins to appear until at one generation all of the sample is of intermediate mass (Hybrid DNA). At longer times a band of light DNA appears, until at 4.1 generations the sample is almost all lighter DNA. This is exactly what is predicted by the Watson-Crick semiconservative mechanism. From Meselson and Stahl (1958)

Meselson and Stahl stated their results as follows, “The nitrogen of DNA is divided equally between two subunits which remain intact through many generations…. Following replication, each daughter molecule has received one parental subunit” (p. 676).

Meselson and Stahl also noted the implications of their work for deciding among the proposed mechanisms for DNA replication. In a section labeled “The Watson-Crick Model” they noted that, “This [the structure of the DNA molecule] suggested to Watson and Crick a definite and structurally plausible hypothesis for the duplication of the DNA molecule. According to this idea, the two chains separate, exposing the hydrogen-bonding sites of the bases. Then, in accord with base-pairing restrictions, each chain serves as a template for the synthesis of its complement. Accordingly, each daughter molecule contains one of the parental chains paired with a newly synthesized chain…. The results of the present experiment are in exact accord with the expectations of the Watson-Crick model for DNA replication” (pp. 677–678).

It also showed that the dispersive replication mechanism proposed by Delbruck, which had smaller subunits, was incorrect. “Since the apparent molecular weight of the subunits so obtained is found to be close to half that of the intact molecule, it may be further concluded that the subunits of the DNA molecule which are conserved at duplication are single, continuous structures. The scheme for DNA duplication proposed by Delbruck is thereby ruled out” (p. 681). Later work by John Cairns and others showed that the subunits of DNA were the entire single polynucleotide chains of the Watson-Crick model of DNA structure.

The Meselson-Stahl experiment is a crucial experiment in biology. It decided between three proposed mechanisms for the replication of DNA. It supported the Watson-Crick semiconservative mechanism and eliminated the conservative and dispersive mechanisms. It played a similar role in biology to that of the experiments that demonstrated the nonconservation of parity did in physics. Thus, we have seen evidence that experiment plays similar roles in both biology and physics and also that the same epistemological strategies are used in both disciplines.

One interesting recent development in science, and thus in the philosophy of science, has been the increasing use of, and importance of, computer simulations. In some fields, such as high-energy physics, simulations are an essential part of all experiments. It is fair to say that without computer simulations these experiments would be impossible. There has been a considerable literature in the philosophy of science discussing whether computer simulations are experiments, theory, or some new kind of hybrid method of doing science. But, as Eric Winsberg remarked, “We have in other words, rejected the overly conservative intuition that computer simulation is nothing but boring and straightforward theory application. But we have avoided embracing the opposite, overly grandiose intuition that simulation is a radically new kind of knowledge production, ”on a par“ with experimentation. In fact, we have seen that soberly locating simulation ”on the methodological map“ is not a simple matter (Winsberg 2010, p. 136).”

Given the importance of computer simulations in science it is essential that we have good reasons to believe their results. Eric Winsberg (2010), Wendy Parker (2008) and others have shown that scientists use strategies quite similar to those discussed in Section 1.1.1, to argue for the correctness of computer simulations.

In this entry varying views on the nature of experimental results have been presented. Some argue that the acceptance of experimental results is based on epistemological arguments, whereas others base acceptance on future utility, social interests, or agreement with existing community commitments. Everyone agrees , however, that for whatever reasons, a consensus is reached on experimental results. These results then play many important roles in physics and we have examined several of these roles, although certainly not all of them. We have seen experiment deciding between two competing theories, calling for a new theory, confirming a theory, refuting a theory, providing evidence that determined the mathematical form of a theory, and providing evidence for the existence of an elementary particle involved in an accepted theory. We have also seen that experiment has a life of its own, independent of theory. If, as I believe, epistemological procedures provide grounds for reasonable belief in experimental results, then experiment can legitimately play the roles I have discussed and can provide the basis for scientific knowledge.

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How to cite this entry . Preview the PDF version of this entry at the Friends of the SEP Society . Look up topics and thinkers related to this entry at the Internet Philosophy Ontology Project (InPhO). Enhanced bibliography for this entry at PhilPapers , with links to its database.

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confirmation | logic: inductive | rationalism vs. empiricism | scientific method | scientific realism

Acknowledgments

We are grateful to Professor Carl Craver for both his comments on the manuscript and for his suggestions for further reading.

Copyright © 2023 by Allan Franklin < allan . franklin @ colorado . edu > Slobodan Perovic < sperovic @ f . bg . ac . rs >

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Science and the scientific method: Definitions and examples

Here's a look at the foundation of doing science — the scientific method.

The scientific method

Hypothesis, theory and law, a brief history of science, additional resources, bibliography.

Science is a systematic and logical approach to discovering how things in the universe work. It is also the body of knowledge accumulated through the discoveries about all the things in the universe. 

The word "science" is derived from the Latin word "scientia," which means knowledge based on demonstrable and reproducible data, according to the Merriam-Webster dictionary . True to this definition, science aims for measurable results through testing and analysis, a process known as the scientific method. Science is based on fact, not opinion or preferences. The process of science is designed to challenge ideas through research. One important aspect of the scientific process is that it focuses only on the natural world, according to the University of California, Berkeley . Anything that is considered supernatural, or beyond physical reality, does not fit into the definition of science.

When conducting research, scientists use the scientific method to collect measurable, empirical evidence in an experiment related to a hypothesis (often in the form of an if/then statement) that is designed to support or contradict a scientific theory .

"As a field biologist, my favorite part of the scientific method is being in the field collecting the data," Jaime Tanner, a professor of biology at Marlboro College, told Live Science. "But what really makes that fun is knowing that you are trying to answer an interesting question. So the first step in identifying questions and generating possible answers (hypotheses) is also very important and is a creative process. Then once you collect the data you analyze it to see if your hypothesis is supported or not."

The steps of the scientific method go something like this, according to Highline College :

• Make an observation or observations.
• Form a hypothesis — a tentative description of what's been observed, and make predictions based on that hypothesis.
• Test the hypothesis and predictions in an experiment that can be reproduced.
• Analyze the data and draw conclusions; accept or reject the hypothesis or modify the hypothesis if necessary.
• Reproduce the experiment until there are no discrepancies between observations and theory. "Replication of methods and results is my favorite step in the scientific method," Moshe Pritsker, a former post-doctoral researcher at Harvard Medical School and CEO of JoVE, told Live Science. "The reproducibility of published experiments is the foundation of science. No reproducibility — no science."

Some key underpinnings to the scientific method:

• The hypothesis must be testable and falsifiable, according to North Carolina State University . Falsifiable means that there must be a possible negative answer to the hypothesis.
• Research must involve deductive reasoning and inductive reasoning . Deductive reasoning is the process of using true premises to reach a logical true conclusion while inductive reasoning uses observations to infer an explanation for those observations.
• An experiment should include a dependent variable (which does not change) and an independent variable (which does change), according to the University of California, Santa Barbara .
• An experiment should include an experimental group and a control group. The control group is what the experimental group is compared against, according to Britannica .

The process of generating and testing a hypothesis forms the backbone of the scientific method. When an idea has been confirmed over many experiments, it can be called a scientific theory. While a theory provides an explanation for a phenomenon, a scientific law provides a description of a phenomenon, according to The University of Waikato . One example would be the law of conservation of energy, which is the first law of thermodynamics that says that energy can neither be created nor destroyed. 

A law describes an observed phenomenon, but it doesn't explain why the phenomenon exists or what causes it. "In science, laws are a starting place," said Peter Coppinger, an associate professor of biology and biomedical engineering at the Rose-Hulman Institute of Technology. "From there, scientists can then ask the questions, 'Why and how?'"

Laws are generally considered to be without exception, though some laws have been modified over time after further testing found discrepancies. For instance, Newton's laws of motion describe everything we've observed in the macroscopic world, but they break down at the subatomic level.

This does not mean theories are not meaningful. For a hypothesis to become a theory, scientists must conduct rigorous testing, typically across multiple disciplines by separate groups of scientists. Saying something is "just a theory" confuses the scientific definition of "theory" with the layperson's definition. To most people a theory is a hunch. In science, a theory is the framework for observations and facts, Tanner told Live Science.

The earliest evidence of science can be found as far back as records exist. Early tablets contain numerals and information about the solar system , which were derived by using careful observation, prediction and testing of those predictions. Science became decidedly more "scientific" over time, however.

1200s: Robert Grosseteste developed the framework for the proper methods of modern scientific experimentation, according to the Stanford Encyclopedia of Philosophy. His works included the principle that an inquiry must be based on measurable evidence that is confirmed through testing.

1400s: Leonardo da Vinci began his notebooks in pursuit of evidence that the human body is microcosmic. The artist, scientist and mathematician also gathered information about optics and hydrodynamics.

1500s: Nicolaus Copernicus advanced the understanding of the solar system with his discovery of heliocentrism. This is a model in which Earth and the other planets revolve around the sun, which is the center of the solar system.

1600s: Johannes Kepler built upon those observations with his laws of planetary motion. Galileo Galilei improved on a new invention, the telescope, and used it to study the sun and planets. The 1600s also saw advancements in the study of physics as Isaac Newton developed his laws of motion.

1700s: Benjamin Franklin discovered that lightning is electrical. He also contributed to the study of oceanography and meteorology. The understanding of chemistry also evolved during this century as Antoine Lavoisier, dubbed the father of modern chemistry , developed the law of conservation of mass.

1800s: Milestones included Alessandro Volta's discoveries regarding electrochemical series, which led to the invention of the battery. John Dalton also introduced atomic theory, which stated that all matter is composed of atoms that combine to form molecules. The basis of modern study of genetics advanced as Gregor Mendel unveiled his laws of inheritance. Later in the century, Wilhelm Conrad Röntgen discovered X-rays , while George Ohm's law provided the basis for understanding how to harness electrical charges.

1900s: The discoveries of Albert Einstein , who is best known for his theory of relativity, dominated the beginning of the 20th century. Einstein's theory of relativity is actually two separate theories. His special theory of relativity, which he outlined in a 1905 paper, " The Electrodynamics of Moving Bodies ," concluded that time must change according to the speed of a moving object relative to the frame of reference of an observer. His second theory of general relativity, which he published as " The Foundation of the General Theory of Relativity ," advanced the idea that matter causes space to curve.

In 1952, Jonas Salk developed the polio vaccine , which reduced the incidence of polio in the United States by nearly 90%, according to Britannica . The following year, James D. Watson and Francis Crick discovered the structure of DNA , which is a double helix formed by base pairs attached to a sugar-phosphate backbone, according to the National Human Genome Research Institute .

2000s: The 21st century saw the first draft of the human genome completed, leading to a greater understanding of DNA. This advanced the study of genetics, its role in human biology and its use as a predictor of diseases and other disorders, according to the National Human Genome Research Institute .

• This video from City University of New York delves into the basics of what defines science.
• Learn about what makes science science in this book excerpt from Washington State University .
• This resource from the University of Michigan — Flint explains how to design your own scientific study.

Merriam-Webster Dictionary, Scientia. 2022. https://www.merriam-webster.com/dictionary/scientia

University of California, Berkeley, "Understanding Science: An Overview." 2022. ​​ https://undsci.berkeley.edu/article/0_0_0/intro_01  

Highline College, "Scientific method." July 12, 2015. https://people.highline.edu/iglozman/classes/astronotes/scimeth.htm  

North Carolina State University, "Science Scripts." https://projects.ncsu.edu/project/bio183de/Black/science/science_scripts.html  

University of California, Santa Barbara. "What is an Independent variable?" October 31,2017. http://scienceline.ucsb.edu/getkey.php?key=6045  

Encyclopedia Britannica, "Control group." May 14, 2020. https://www.britannica.com/science/control-group  

The University of Waikato, "Scientific Hypothesis, Theories and Laws." https://sci.waikato.ac.nz/evolution/Theories.shtml  

Stanford Encyclopedia of Philosophy, Robert Grosseteste. May 3, 2019. https://plato.stanford.edu/entries/grosseteste/  

Encyclopedia Britannica, "Jonas Salk." October 21, 2021. https://www.britannica.com/ biography /Jonas-Salk

National Human Genome Research Institute, "​Phosphate Backbone." https://www.genome.gov/genetics-glossary/Phosphate-Backbone  

National Human Genome Research Institute, "What is the Human Genome Project?" https://www.genome.gov/human-genome-project/What  

‌ Live Science contributor Ashley Hamer updated this article on Jan. 16, 2022.

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Meaning of experiment – Learner’s Dictionary

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• scientific experiments
• inhumane experiments on monkeys
• The table below shows the results of the experiment.
• Parallel experiments are being conducted in both countries .
• There is a growing debate on medical experiments.

experiment verb [I] ( TRY SOMETHING )

Experiment verb [i] ( do tests ).

• experimentation

(Definition of experiment from the Cambridge Learner's Dictionary © Cambridge University Press)

Translations of experiment

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Observing the natural world and paying attention to its patterns has been part of human history from the very beginning. However, studying nature to understand it purely for its own sake seems to have had its start among the pre-Socratic philosophers of the 6th century BCE, such as Thales and Anaximander .

How is science related to math?

Science uses mathematics extensively as a powerful tool in the further understanding of phenomena. Sometimes scientific discoveries have inspired mathematicians, and at other times scientists have realized that forms of mathematics that were developed without any regard for their usefulness could be applied to understanding the physical world.

All peoples have studied the natural world, but most ancient peoples studied it for practical purposes, such as paying attention to natural cycles to know when to plant crops. It does not seem to have been until the 6th century BCE that the pre-Socratic philosophers (who lived in what is now Turkey and Greece) began seeking to understand nature as an end in itself.

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science , any system of knowledge that is concerned with the physical world and its phenomena and that entails unbiased observations and systematic experimentation. In general, a science involves a pursuit of knowledge covering general truths or the operations of fundamental laws.

Science can be divided into different branches based on the subject of study. The physical sciences study the inorganic world and comprise the fields of astronomy , physics , chemistry , and the Earth sciences . The biological sciences such as biology and medicine study the organic world of life and its processes. Social sciences like anthropology and economics study the social and cultural aspects of human behaviour .

Science is further treated in a number of articles. For the history of Western and Eastern science, see science, history of . For the conceptualization of science and its interrelationships with culture , see science, philosophy of . For the basic aspects of the scientific approach, see physical science, principles of ; and scientific method .

Look up a word, learn it forever.

Other forms: experiments; experimenting; experimented

If you see your science-loving neighbor headed home with a power cord, a handful of test tubes, a stopwatch, and a bag of potatoes, there’s probably no need to be alarmed. There’s a good chance he’s only conducting an experiment , a scientific test conducted under controlled conditions.

To refer to a scientific test, use the noun experiment . If you want to describe the work done in conducting such a test, experiment will do the trick as well, since it can also act as a verb, as in "scientists experiment with helium." You can also use it more generally to describe trying a new method or idea. For example, you could experiment with a new hairstyle or different routes to get to school or work.

• noun the act of conducting a controlled test or investigation synonyms: experimentation see more see less types: show 4 types... hide 4 types... testing the act of subjecting to experimental test in order to determine how well something works trial and error experimenting until a solution is found Michelson-Morley experiment a celebrated experiment conducted by Albert Michelson and Edward Morley; their failure to detect any influence of the earth's motion on the velocity of light was the starting point for Einstein's theory of relativity control experiment an experiment designed to control for variables affecting the results of another experiment type of: research project , scientific research research into questions posed by scientific theories and hypotheses
• noun the testing of an idea “it was an experiment in living” synonyms: experimentation see more see less types: show 7 types... hide 7 types... pilot experiment a preliminary experiment whose outcome can lead to a more extensive experiment test , trial , trial run , tryout trying something to find out about it field test , field trial a test of the performance of some new product under the conditions in which it will be used alpha test (computer science) a first test of an experimental product (such as computer software) carried out by the developer beta test (computer science) a second test of an experimental product (such as computer software) carried out by an outside organization road test a test to insure that a vehicle is roadworthy trial balloon a test of public opinion type of: enquiry , inquiry , research a search for knowledge
• noun a venture at something new or different “as an experiment he decided to grow a beard” see more see less type of: venture any venturesome undertaking especially one with an uncertain outcome
• verb conduct a test or investigation “We are experimenting with the new drug in order to fight this disease” synonyms: try out try something new, as in order to gain experience see more see less type of: investigate , look into investigate scientifically
• verb try something new, as in order to gain experience “The composer experimented with a new style” synonyms: try out

Vocabulary lists containing experiment

How can you perform well on the reading section of the SAT if you don’t fully understand the language being used in the directions and in the questions? Learn this list of 25 words that are based on our analysis of the words likely to appear in question stems, answer options, and test directions. Following our Roadmap to the SAT ? Head back to see what else you should be learning this week.

How can you perform well on the new reading section of the SAT if you don’t fully understand the language being used in the directions and in the questions? Learn this list of 150 words that are based on our analysis of the words likely to appear in question stems, answer options and test directions. Here are all of our word lists to help you prepare for the new SAT (debuting March of 2016): The Language of the Test , Multiple-Meaning Words , and Words to Capture Tone .

What Is a Variable in Science?

Understanding Variables in a Science Experiment

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Variables are an important part of science projects and experiments. What is a variable? Basically, a variable is any factor that can be controlled, changed, or measured in an experiment. Scientific experiments have several types of variables. The independent and dependent variables are the ones usually plotted on a chart or graph, but there are other types of variables you may encounter.

Types of Variables

• Independent Variable: The independent variable is the one condition that you change in an experiment. Example: In an experiment measuring the effect of temperature on solubility, the independent variable is temperature.
• Dependent Variable: The dependent variable is the variable that you measure or observe. The dependent variable gets its name because it is the factor that is dependent on the state of the independent variable . Example: In the experiment measuring the effect of temperature on solubility, solubility would be the dependent variable.
• Controlled Variable: A controlled variable or constant variable is a variable that does not change during an experiment. Example : In the experiment measuring the effect of temperature on solubility, controlled variable could include the source of water used in the experiment, the size and type of containers used to mix chemicals, and the amount of mixing time allowed for each solution.
• Extraneous Variables: Extraneous variables are "extra" variables that may influence the outcome of an experiment but aren't taken into account during measurement. Ideally, these variables won't impact the final conclusion drawn by the experiment, but they may introduce error into scientific results. If you are aware of any extraneous variables, you should enter them in your lab notebook . Examples of extraneous variables include accidents, factors you either can't control or can't measure, and factors you consider unimportant. Every experiment has extraneous variables. Example : You are conducting an experiment to see which paper airplane design flies longest. You may consider the color of the paper to be an extraneous variable. You note in your lab book that different colors of papers were used. Ideally, this variable does not affect your outcome.

Using Variables in Science Experiment

In a science experiment , only one variable is changed at a time (the independent variable) to test how this changes the dependent variable. The researcher may measure other factors that either remain constant or change during the course of the experiment but are not believed to affect its outcome. These are controlled variables. Any other factors that might be changed if someone else conducted the experiment but seemed unimportant should also be noted. Also, any accidents that occur should be recorded. These are extraneous variables.

Variables and Attributes

In science, when a variable is studied, its attribute is recorded. A variable is a characteristic, while an attribute is its state. For example, if eye color is the variable, its attribute might be green, brown, or blue. If height is the variable, its attribute might be 5 m, 2.5 cm, or 1.22 km.

• Earl R. Babbie. The Practice of Social Research , 12th edition. Wadsworth Publishing, 2009.
• What Is a Dependent Variable?
• What Is an Experiment? Definition and Design
• Six Steps of the Scientific Method
• Examples of Independent and Dependent Variables
• How To Design a Science Fair Experiment
• The Role of a Controlled Variable in an Experiment
• Scientific Variable
• What Are the Elements of a Good Hypothesis?
• Dependent Variable vs. Independent Variable: What Is the Difference?
• What Is the Difference Between a Control Variable and Control Group?
• Independent Variable Definition and Examples
• Null Hypothesis Examples
• What Is a Controlled Experiment?
• DRY MIX Experiment Variables Acronym
• Scientific Method Vocabulary Terms
• What Is the Difference Between Hard and Soft Science?

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The state-of-the-art of mycobacterium chimaera infections and the causal link with health settings: a systematic review.

1. Introduction

2. materials and methods, 4. discussion, 4.1. mycobacterium chimaera’s characteristics and ecosystem, 4.2. heater-cooler units, medical devices, water, and air-conditioned implants, 4.3. incubation period and symptoms presentation, 4.4. presence in the lung system, 4.5. modality of transmission, 4.6. detection, 4.7. disinfection, 4.8. causal link assessment, 5. limitations, 6. conclusions, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest, abbreviations.

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Bolcato, V.; Bassetti, M.; Basile, G.; Bianco Prevot, L.; Speziale, G.; Tremoli, E.; Maffessanti, F.; Tronconi, L.P. The State-of-the-Art of Mycobacterium chimaera Infections and the Causal Link with Health Settings: A Systematic Review. Healthcare 2024 , 12 , 1788. https://doi.org/10.3390/healthcare12171788

Bolcato V, Bassetti M, Basile G, Bianco Prevot L, Speziale G, Tremoli E, Maffessanti F, Tronconi LP. The State-of-the-Art of Mycobacterium chimaera Infections and the Causal Link with Health Settings: A Systematic Review. Healthcare . 2024; 12(17):1788. https://doi.org/10.3390/healthcare12171788

Bolcato, Vittorio, Matteo Bassetti, Giuseppe Basile, Luca Bianco Prevot, Giuseppe Speziale, Elena Tremoli, Francesco Maffessanti, and Livio Pietro Tronconi. 2024. "The State-of-the-Art of Mycobacterium chimaera Infections and the Causal Link with Health Settings: A Systematic Review" Healthcare 12, no. 17: 1788. https://doi.org/10.3390/healthcare12171788

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