control experiment parts

Controlled Experiment

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

This is when a hypothesis is scientifically tested.

In a controlled experiment, an independent variable (the cause) is systematically manipulated, and the dependent variable (the effect) is measured; any extraneous variables are controlled.

The researcher can operationalize (i.e., define) the studied variables so they can be objectively measured. The quantitative data can be analyzed to see if there is a difference between the experimental and control groups.

What is the control group?

In experiments scientists compare a control group and an experimental group that are identical in all respects, except for one difference – experimental manipulation.

Unlike the experimental group, the control group is not exposed to the independent variable under investigation and so provides a baseline against which any changes in the experimental group can be compared.

Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to experimental manipulation rather than chance.

Randomly allocating participants to independent variable groups means that all participants should have an equal chance of participating in each condition.

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

What are extraneous variables?

The researcher wants to ensure that the manipulation of the independent variable has changed the changes in the dependent variable.

Hence, all the other variables that could affect the dependent variable to change must be controlled. These other variables are called extraneous or confounding variables.

Extraneous variables should be controlled were possible, as they might be important enough to provide alternative explanations for the effects.

In practice, it would be difficult to control all the variables in a child’s educational achievement. For example, it would be difficult to control variables that have happened in the past.

A researcher can only control the current environment of participants, such as time of day and noise levels.

Why conduct controlled experiments?

Scientists use controlled experiments because they allow for precise control of extraneous and independent variables. This allows a cause-and-effect relationship to be established.

Controlled experiments also follow a standardized step-by-step procedure. This makes it easy for another researcher to replicate the study.

Key Terminology

Experimental group.

The group being treated or otherwise manipulated for the sake of the experiment.

Control Group

They receive no treatment and are used as a comparison group.

Ecological validity

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

Experimenter effects

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

Demand characteristics

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

Independent variable (IV)

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

Dependent variable (DV)

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

Extraneous variables (EV)

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

Confounding variables

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

Random Allocation

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

Order effects

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

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

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

What is the control in an experiment?

In an experiment , the control is a standard or baseline group not exposed to the experimental treatment or manipulation. It serves as a comparison group to the experimental group, which does receive the treatment or manipulation.

The control group helps to account for other variables that might influence the outcome, allowing researchers to attribute differences in results more confidently to the experimental treatment.

Establishing a cause-and-effect relationship between the manipulated variable (independent variable) and the outcome (dependent variable) is critical in establishing a cause-and-effect relationship between the manipulated variable.

What is the purpose of controlling the environment when testing a hypothesis?

Controlling the environment when testing a hypothesis aims to eliminate or minimize the influence of extraneous variables. These variables other than the independent variable might affect the dependent variable, potentially confounding the results.

By controlling the environment, researchers can ensure that any observed changes in the dependent variable are likely due to the manipulation of the independent variable, not other factors.

This enhances the experiment’s validity, allowing for more accurate conclusions about cause-and-effect relationships.

It also improves the experiment’s replicability, meaning other researchers can repeat the experiment under the same conditions to verify the results.

Why are hypotheses important to controlled experiments?

Hypotheses are crucial to controlled experiments because they provide a clear focus and direction for the research. A hypothesis is a testable prediction about the relationship between variables.

It guides the design of the experiment, including what variables to manipulate (independent variables) and what outcomes to measure (dependent variables).

The experiment is then conducted to test the validity of the hypothesis. If the results align with the hypothesis, they provide evidence supporting it.

The hypothesis may be revised or rejected if the results do not align. Thus, hypotheses are central to the scientific method, driving the iterative inquiry, experimentation, and knowledge advancement process.

What is the experimental method?

The experimental method is a systematic approach in scientific research where an independent variable is manipulated to observe its effect on a dependent variable, under controlled conditions.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology
• Controlled Experiments | Methods & Examples of Control

Controlled Experiments | Methods & Examples of Control

Published on 19 April 2022 by Pritha Bhandari . Revised on 10 October 2022.

In experiments , researchers manipulate independent variables to test their effects on dependent variables. In a controlled experiment , all variables other than the independent variable are controlled or held constant so they don’t influence the dependent variable.

Controlling variables can involve:

• Holding variables at a constant or restricted level (e.g., keeping room temperature fixed)
• Measuring variables to statistically control for them in your analyses
• Balancing variables across your experiment through randomisation (e.g., using a random order of tasks)

Table of contents

Why does control matter in experiments, methods of control, problems with controlled experiments, frequently asked questions about controlled experiments.

Control in experiments is critical for internal validity , which allows you to establish a cause-and-effect relationship between variables.

• Your independent variable is the colour used in advertising.
• Your dependent variable is the price that participants are willing to pay for a standard fast food meal.

Extraneous variables are factors that you’re not interested in studying, but that can still influence the dependent variable. For strong internal validity, you need to remove their effects from your experiment.

• Design and description of the meal
• Study environment (e.g., temperature or lighting)
• Participant’s frequency of buying fast food
• Participant’s familiarity with the specific fast food brand
• Participant’s socioeconomic status

Prevent plagiarism, run a free check.

You can control some variables by standardising your data collection procedures. All participants should be tested in the same environment with identical materials. Only the independent variable (e.g., advert colour) should be systematically changed between groups.

Other extraneous variables can be controlled through your sampling procedures . Ideally, you’ll select a sample that’s representative of your target population by using relevant inclusion and exclusion criteria (e.g., including participants from a specific income bracket, and not including participants with colour blindness).

By measuring extraneous participant variables (e.g., age or gender) that may affect your experimental results, you can also include them in later analyses.

After gathering your participants, you’ll need to place them into groups to test different independent variable treatments. The types of groups and method of assigning participants to groups will help you implement control in your experiment.

Control groups

Controlled experiments require control groups . Control groups allow you to test a comparable treatment, no treatment, or a fake treatment, and compare the outcome with your experimental treatment.

You can assess whether it’s your treatment specifically that caused the outcomes, or whether time or any other treatment might have resulted in the same effects.

• A control group that’s presented with red advertisements for a fast food meal
• An experimental group that’s presented with green advertisements for the same fast food meal

Random assignment

To avoid systematic differences between the participants in your control and treatment groups, you should use random assignment .

This helps ensure that any extraneous participant variables are evenly distributed, allowing for a valid comparison between groups .

Random assignment is a hallmark of a ‘true experiment’ – it differentiates true experiments from quasi-experiments .

Masking (blinding)

Masking in experiments means hiding condition assignment from participants or researchers – or, in a double-blind study , from both. It’s often used in clinical studies that test new treatments or drugs.

Sometimes, researchers may unintentionally encourage participants to behave in ways that support their hypotheses. In other cases, cues in the study environment may signal the goal of the experiment to participants and influence their responses.

Using masking means that participants don’t know whether they’re in the control group or the experimental group. This helps you control biases from participants or researchers that could influence your study results.

Although controlled experiments are the strongest way to test causal relationships, they also involve some challenges.

Difficult to control all variables

Especially in research with human participants, it’s impossible to hold all extraneous variables constant, because every individual has different experiences that may influence their perception, attitudes, or behaviors.

But measuring or restricting extraneous variables allows you to limit their influence or statistically control for them in your study.

Risk of low external validity

Controlled experiments have disadvantages when it comes to external validity – the extent to which your results can be generalised to broad populations and settings.

The more controlled your experiment is, the less it resembles real world contexts. That makes it harder to apply your findings outside of a controlled setting.

There’s always a tradeoff between internal and external validity . It’s important to consider your research aims when deciding whether to prioritise control or generalisability in your experiment.

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

To design a successful experiment, first identify:

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

When designing the experiment, first decide:

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

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

Bhandari, P. (2022, October 10). Controlled Experiments | Methods & Examples of Control. Scribbr. Retrieved 9 September 2024, from https://www.scribbr.co.uk/research-methods/controlled-experiments/

Is this article helpful?

Pritha Bhandari

• COVID-19 Tracker
• Biochemistry
• Anatomy & Physiology
• Microbiology
• Neuroscience
• Animal Kingdom
• NGSS High School
• Latest News
• Editors’ Picks
• Weekly Digest
• Quotes about Biology

Controlled Experiment

Reviewed by: BD Editors

Controlled Experiment Definition

A controlled experiment is a scientific test that is directly manipulated by a scientist, in order to test a single variable at a time. The variable being tested is the independent variable , and is adjusted to see the effects on the system being studied. The controlled variables are held constant to minimize or stabilize their effects on the subject. In biology, a controlled experiment often includes restricting the environment of the organism being studied. This is necessary to minimize the random effects of the environment and the many variables that exist in the wild.

In a controlled experiment, the study population is often divided into two groups. One group receives a change in a certain variable, while the other group receives a standard environment and conditions. This group is referred to as the control group , and allows for comparison with the other group, known as the experimental group . Many types of controls exist in various experiments, which are designed to ensure that the experiment worked, and to have a basis for comparison. In science, results are only accepted if it can be shown that they are statistically significant . Statisticians can use the difference between the control group and experimental group and the expected difference to determine if the experiment supports the hypothesis , or if the data was simply created by chance.

Examples of Controlled Experiment

Music preference in dogs.

Do dogs have a taste in music? You might have considered this, and science has too. Believe it or not, researchers have actually tested dog’s reactions to various music genres. To set up a controlled experiment like this, scientists had to consider the many variables that affect each dog during testing. The environment the dog is in when listening to music, the volume of the music, the presence of humans, and even the temperature were all variables that the researches had to consider.

In this case, the genre of the music was the independent variable. In other words, to see if dog’s change their behavior in response to different kinds of music, a controlled experiment had to limit the interaction of the other variables on the dogs. Usually, an experiment like this is carried out in the same location, with the same lighting, furniture, and conditions every time. This ensures that the dogs are not changing their behavior in response to the room. To make sure the dogs don’t react to humans or simply the noise of the music, no one else can be in the room and the music must be played at the same volume for each genre. Scientist will develop protocols for their experiment, which will ensure that many other variables are controlled.

This experiment could also split the dogs into two groups, only testing music on one group. The control group would be used to set a baseline behavior, and see how dogs behaved without music. The other group could then be observed and the differences in the group’s behavior could be analyzed. By rating behaviors on a quantitative scale, statistics can be used to analyze the difference in behavior, and see if it was large enough to be considered significant. This basic experiment was carried out on a large number of dogs, analyzing their behavior with a variety of different music genres. It was found that dogs do show more relaxed and calm behaviors when a specific type of music plays. Come to find out, dogs enjoy reggae the most.

Scurvy in Sailors

In the early 1700s, the world was a rapidly expanding place. Ships were being built and sent all over the world, carrying thousands and thousands of sailors. These sailors were mostly fed the cheapest diets possible, not only because it decreased the costs of goods, but also because fresh food is very hard to keep at sea. Today, we understand that lack of essential vitamins and nutrients can lead to severe deficiencies that manifest as disease. One of these diseases is scurvy.

Scurvy is caused by a simple vitamin C deficiency, but the effects can be brutal. Although early symptoms just include general feeling of weakness, the continued lack of vitamin C will lead to a breakdown of the blood cells and vessels that carry the blood. This results in blood leaking from the vessels. Eventually, people bleed to death internally and die. Before controlled experiments were commonplace, a simple physician decided to tackle the problem of scurvy. James Lind, of the Royal Navy, came up with a simple controlled experiment to find the best cure for scurvy.

He separated sailors with scurvy into various groups. He subjected them to the same controlled condition and gave them the same diet, except one item. Each group was subjected to a different treatment or remedy, taken with their food. Some of these remedies included barley water, cider and a regiment of oranges and lemons. This created the first clinical trial , or test of the effectiveness of certain treatments in a controlled experiment. Lind found that the oranges and lemons helped the sailors recover fast, and within a few years the Royal Navy had developed protocols for growing small leafy greens that contained high amounts of vitamin C to feed their sailors.

Related Biology Terms

• Field Experiment – An experiment conducted in nature, outside the bounds of total control.
• Independent Variable – The thing in an experiment being changed or manipulated by the experimenter to see effects on the subject.
• Controlled Variable – A thing that is normalized or standardized across an experiment, to remove it from having an effect on the subject being studied.
• Control Group – A group of subjects in an experiment that receive no independent variable, or a normalized amount, to provide comparison.

Cite This Article

Subscribe to our newsletter, privacy policy, terms of service, scholarship, latest posts, white blood cell, t cell immunity, satellite cells, embryonic stem cells, popular topics, animal cell, homeostasis, amino acids.

Microbe Notes

Controlled Experiments: Definition, Steps, Results, Uses

Controlled experiments ensure valid and reliable results by minimizing biases and controlling variables effectively.

Rigorous planning, ethical considerations, and precise data analysis are vital for successful experiment execution and meaningful conclusions.

Real-world applications demonstrate the practical impact of controlled experiments, guiding informed decision-making in diverse domains.

Controlled experiments are the systematic research method where variables are intentionally manipulated and controlled to observe the effects of a particular phenomenon. It aims to isolate and measure the impact of specific variables, ensuring a more accurate causality assessment.

Table of Contents

Interesting Science Videos

Importance of controlled experiments in various fields

Controlled experiments are significant across diverse fields, including science, psychology, economics, healthcare, and technology.

They provide a systematic approach to test hypotheses, establish cause-and-effect relationships, and validate the effectiveness of interventions or solutions.

Why Controlled Experiments Matter? 

Validity and reliability of results.

Controlled experiments uphold the gold standard for scientific validity and reliability. By meticulously controlling variables and conditions, researchers can attribute observed outcomes accurately to the independent variable being tested. This precision ensures that the findings can be replicated and are trustworthy.

Minimizing Biases and Confounding Variables

One of the core benefits of controlled experiments lies in their ability to minimize biases and confounding variables. Extraneous factors that could distort results are mitigated through careful control and randomization. This enables researchers to isolate the effects of the independent variable, leading to a more accurate understanding of causality.

Achieving Causal Inference

Controlled experiments provide a strong foundation for establishing causal relationships between variables. Researchers can confidently infer causation by manipulating specific variables and observing resulting changes. The capability informs decision-making, policy formulation, and advancements across various fields.

Planning a Controlled Experiment

Formulating research questions and hypotheses.

Formulating clear research questions and hypotheses is paramount at the outset of a controlled experiment. These inquiries guide the direction of the study, defining the variables of interest and setting the stage for structured experimentation.

Well-defined questions and hypotheses contribute to focused research and facilitate meaningful data collection.

Identifying Variables and Control Groups

Identifying and defining independent, dependent, and control variables is fundamental to experimental planning. 

Precise identification ensures that the experiment is designed to isolate the effect of the independent variable while controlling for other influential factors. Establishing control groups allows for meaningful comparisons and robust analysis of the experimental outcomes.

Designing Experimental Procedures and Protocols

Careful design of experimental procedures and protocols is essential for a successful controlled experiment. The step involves outlining the methodology, data collection techniques, and the sequence of activities in the experiment. 

A well-designed experiment is structured to maintain consistency, control, and accuracy throughout the study, thereby enhancing the validity and credibility of the results.

Conducting a Controlled Experiment

Randomization and participant selection.

Randomization is a critical step in ensuring the fairness and validity of a controlled experiment. It involves assigning participants to different experimental conditions in a random and unbiased manner. 

The selection of participants should accurately represent the target population, enhancing the results’ generalizability.

Data Collection Methods and Instruments

Selecting appropriate data collection methods and instruments is pivotal in gathering accurate and relevant data. Researchers often employ surveys, observations, interviews, or specialized tools to record and measure the variables of interest. 

The chosen methods should align with the experiment’s objectives and provide reliable data for analysis.

Monitoring and Maintaining Experimental Conditions

Maintaining consistent and controlled experimental conditions throughout the study is essential. Regular monitoring helps ensure that variables remain constant and uncontaminated, reducing the risk of confounding factors. 

Rigorous monitoring protocols and timely adjustments are crucial for the accuracy and reliability of the experiment.

Analysing Results and Drawing Conclusions

Data analysis techniques.

Data analysis involves employing appropriate statistical and analytical techniques to process the collected data. This step helps derive meaningful insights, identify patterns, and draw valid conclusions. 

Common techniques include regression analysis, t-tests , ANOVA , and more, tailored to the research design and data type .

Interpretation of Results

Interpreting the results entails understanding the statistical outcomes and their implications for the research objectives. 

Researchers analyze patterns, trends, and relationships revealed by the data analysis to infer the experiment’s impact on the variables under study. Clear and accurate interpretation is crucial for deriving actionable insights.

Implications and Potential Applications

Identifying the broader implications and potential applications of the experiment’s results is fundamental. Researchers consider how the findings can inform decision-making, policy development, or further research. 

Understanding the practical implications helps bridge the gap between theoretical insights and real-world application.

Common Challenges and Solutions

Addressing ethical considerations.

Ethical challenges in controlled experiments include ensuring informed consent, protecting participants’ privacy, and minimizing harm. 

Solutions involve thorough ethics reviews, transparent communication with participants, and implementing safeguards to uphold ethical standards throughout the experiment.

Dealing with Sample Size and Statistical Power

The sample size is crucial for achieving statistically significant results. Adequate sample sizes enhance the experiment’s power to detect meaningful effects accurately. 

Statistical power analysis guides researchers in determining the optimal sample size for the experiment, minimizing the risk of type I and II errors .

Mitigating Unforeseen Variables

Unforeseen variables can introduce bias and affect the experiment’s validity. Researchers employ meticulous planning and robust control measures to minimize the impact of unforeseen variables. 

Pre-testing and pilot studies help identify potential confounders, allowing researchers to adapt the experiment accordingly.

A controlled experiment involves meticulous planning, precise execution, and insightful analysis. Adhering to ethical standards, optimizing sample size, and adapting to unforeseen variables are key challenges that require thoughtful solutions. 

Real-world applications showcase the transformative potential of controlled experiments across varied domains, emphasizing their indispensable role in evidence-based decision-making and progress.

• https://www.khanacademy.org/science/biology/intro-to-biology/science-of-biology/a/experiments-and-observations
• https://www.scribbr.com/methodology/controlled-experiment/
• https://link.springer.com/10.1007/978-1-4899-7687-1_891
• http://ai.stanford.edu/~ronnyk/GuideControlledExperiments.pdf
• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6776925/
• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4017459/
• https://www.merriam-webster.com/dictionary/controlled%20experiment

About Author

Krisha Karki

Leave a Comment Cancel reply

Save my name, email, and website in this browser for the next time I comment.

What Is a Controlled Experiment?

Definition and Example

• Scientific Method
• Chemical Laws
• Periodic Table
• Projects & Experiments
• Biochemistry
• Physical Chemistry
• Medical Chemistry
• Chemistry In Everyday Life
• Famous Chemists
• Activities for Kids
• Abbreviations & Acronyms
• Weather & Climate
• Ph.D., Biomedical Sciences, University of Tennessee at Knoxville
• B.A., Physics and Mathematics, Hastings College

A controlled experiment is one in which everything is held constant except for one variable . Usually, a set of data is taken to be a control group , which is commonly the normal or usual state, and one or more other groups are examined where all conditions are identical to the control group and to each other except for one variable.

Sometimes it's necessary to change more than one variable, but all of the other experimental conditions will be controlled so that only the variables being examined change. And what is measured is the variables' amount or the way in which they change.

Controlled Experiment

• A controlled experiment is simply an experiment in which all factors are held constant except for one: the independent variable.
• A common type of controlled experiment compares a control group against an experimental group. All variables are identical between the two groups except for the factor being tested.
• The advantage of a controlled experiment is that it is easier to eliminate uncertainty about the significance of the results.

Example of a Controlled Experiment

Let's say you want to know if the type of soil affects how long it takes a seed to germinate, and you decide to set up a controlled experiment to answer the question. You might take five identical pots, fill each with a different type of soil, plant identical bean seeds in each pot, place the pots in a sunny window, water them equally, and measure how long it takes for the seeds in each pot to sprout.

This is a controlled experiment because your goal is to keep every variable constant except the type of soil you use. You control these features.

Why Controlled Experiments Are Important

The big advantage of a controlled experiment is that you can eliminate much of the uncertainty about your results. If you couldn't control each variable, you might end up with a confusing outcome.

For example, if you planted different types of seeds in each of the pots, trying to determine if soil type affected germination, you might find some types of seeds germinate faster than others. You wouldn't be able to say, with any degree of certainty, that the rate of germination was due to the type of soil. It might as well have been due to the type of seeds.

Or, if you had placed some pots in a sunny window and some in the shade or watered some pots more than others, you could get mixed results. The value of a controlled experiment is that it yields a high degree of confidence in the outcome. You know which variable caused or did not cause a change.

Are All Experiments Controlled?

No, they are not. It's still possible to obtain useful data from uncontrolled experiments, but it's harder to draw conclusions based on the data.

An example of an area where controlled experiments are difficult is human testing. Say you want to know if a new diet pill helps with weight loss. You can collect a sample of people, give each of them the pill, and measure their weight. You can try to control as many variables as possible, such as how much exercise they get or how many calories they eat.

However, you will have several uncontrolled variables, which may include age, gender, genetic predisposition toward a high or low metabolism, how overweight they were before starting the test, whether they inadvertently eat something that interacts with the drug, etc.

Scientists try to record as much data as possible when conducting uncontrolled experiments, so they can see additional factors that may be affecting their results. Although it is harder to draw conclusions from uncontrolled experiments, new patterns often emerge that would not have been observable in a controlled experiment.

For example, you may notice the diet drug seems to work for female subjects, but not for male subjects, and this may lead to further experimentation and a possible breakthrough. If you had only been able to perform a controlled experiment, perhaps on male clones alone, you would have missed this connection.

• Box, George E. P., et al.  Statistics for Experimenters: Design, Innovation, and Discovery . Wiley-Interscience, a John Wiley & Soncs, Inc., Publication, 2005. 
• Creswell, John W.  Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research . Pearson/Merrill Prentice Hall, 2008.
• Pronzato, L. "Optimal experimental design and some related control problems". Automatica . 2008.
• Robbins, H. "Some Aspects of the Sequential Design of Experiments". Bulletin of the American Mathematical Society . 1952.
• Understanding Simple vs Controlled Experiments
• What Is the Difference Between a Control Variable and Control Group?
• The Role of a Controlled Variable in an Experiment
• Scientific Variable
• DRY MIX Experiment Variables Acronym
• Six Steps of the Scientific Method
• Scientific Method Vocabulary Terms
• What Are the Elements of a Good Hypothesis?
• Scientific Method Flow Chart
• What Is an Experimental Constant?
• Scientific Hypothesis Examples
• What Are Examples of a Hypothesis?
• What Is a Hypothesis? (Science)
• Null Hypothesis Examples
• What Is a Testable Hypothesis?
• Random Error vs. Systematic Error

• Controlled Experiments: Methods, Examples & Limitations

What happens in experimental research is that the researcher alters the independent variables so as to determine their impacts on the dependent variables. 

Therefore, when the experiment is controlled, you can expect that the researcher will control all other variables except for the independent variables . This is done so that the other variables do not have an influence on the dependent variables. 

In this article, we are going to consider controlled experiment, how important it is in a study, and how it can be designed. But before we dig deep, let us look at the definition of a controlled experiment.

What is a Controlled Experiment?

In a scientific experiment, a controlled experiment is a test that is directly altered by the researcher so that only one variable is studied at a time. The single variable being studied will then be the independent variable.

This independent variable is manipulated by the researcher so that its effect on the hypothesis or data being studied is known. While the researcher studies the single independent variable, the controlled variables are made constant to reduce or balance out their impact on the research.

To achieve a controlled experiment, the research population is mostly distributed into two groups. Then the treatment is administered to one of the two groups, while the other group gets the control conditions. This other group is referred to as the control group.

The control group gets the standard conditions and is placed in the standard environment and it also allows for comparison with the other group, which is referred to as the experimental group or the treatment group. Obtaining the difference between these two groups’ behavior is important because in any scientific experiment, being able to show the statistical significance of the results is the only criterion for the results to be accepted.  

So to determine whether the experiment supports the hypothesis, or if the data is a result of chance, the researcher will check for the difference between the control group and experimental group. Then the results from the differences will be compared with the expected difference.

For example, a researcher may want to answer this question, do dogs also have a music taste? In case you’re wondering too, yes, there are existing studies by researchers on how dogs react to different music genres. 

Back to the example, the researcher may develop a controlled experiment with high consideration on the variables that affect each dog. Some of these variables that may have effects on the dog are; the dog’s environment when listening to music, the temperature of the environment, the music volume, and human presence. 

The independent variable to focus on in this research is the genre of the music. To determine if there is an effect on the dog while listening to different kinds of music, the dog’s environment must be controlled. A controlled experiment would limit interaction between the dog and other variables. 

In this experiment, the researcher can also divide the dogs into two groups, one group will perform the music test while the other, the control group will be used as the baseline or standard behavior. The control group behavior can be observed along with the treatment group and the differences in the two group’s behavior can be analyzed. 

What is an Experimental Control?

Experimental control is the technique used by the researcher in scientific research to minimize the effects of extraneous variables. Experimental control also strengthens the ability of the independent variable to change the dependent variable.

For example, the cause and effect possibilities will be examined in a well-designed and properly controlled experiment if the independent variable (Treatment Y) causes a behavioral change in the dependent variable (Subject X).

In another example, a researcher feeds 20 lab rats with an artificial sweetener and from the researcher’s observation, six of the rats died of dehydration. Now, the actual cause of death may be artificial sweeteners or an unrelated factor. Such as the water supplied to the rats being contaminated or the rats could not drink enough, or suffering a disease. 

Read: Nominal, Ordinal, Interval & Ratio Variable + [Examples]

For a researcher, eliminating these potential causes one after the other will consume time, and be tedious. Hence, the researcher can make use of experimental control. This method will allow the researcher to divide the rats into two groups: one group will receive the artificial sweetener while the other one doesn’t. The two groups will be placed in similar conditions and observed in similar ways. The differences that now occur in morbidity between the two groups can be traced to the sweetener with certainty.

From the example above, the experimental control is administered as a form of a control group. The data from the control group is then said to be the standard against which every other experimental outcome is measured.

Purpose & Importance of Control in Experimentation

1. One significant purpose of experimental controls is that it allows researchers to eliminate various confounding variables or uncertainty in their research. A researcher will need to use an experimental control to ensure that only the variables that are intended to change, are changed in research.  

2. Controlled experiments also allow researchers to control the specific variables they think might have an effect on the outcomes of the study. The researcher will use a control group if he/she believes some extra variables can form an effect on the results of the study. This is to ensure that the extra variable is held constant and possible influences are measured.  

3. Controlled experiments establish a standard that the outcome of a study should be compared to, and allow researchers to correct for potential errors. 

Read more: What are Cross-Sectional Studies: Examples, Definition, Types

Methods of Experimental Control

Here are some methods used to achieve control in experimental research

• Use of Control Groups

Control groups are required for controlled experiments. Control groups will allow the researcher to run a test on fake treatment, and comparable treatment. It will also compare the result of the comparison with the researcher’s experimental treatment. The results will allow the researcher to understand if the treatment administered caused the outcome or if other factors such as time, or others are involved and whether they would have yielded the same effects.  

For an example of a control group experiment, a researcher conducting an experiment on the effects of colors in advertising, asked all the participants to come individually to a lab. In this lab,  environmental conditions are kept the same all through the research.

For the researcher to determine the effect of colors in advertising, each of the participants is placed in either of the two groups: the control group or the experimental group.

In the control group, the advertisement color is yellow to represent the clothing industry while blue is given as the advertisement color to the experimental group to represent the clothing industry also. The only difference in these two groups will be the color of the advertisement, other variables will be similar.

• Use of Masking (blinding)

Masking occurs in an experiment when the researcher hides condition assignments from the participants.  If it’s double-blind research, both the researcher and the participants will be in the dark. Masking or blinding is mostly used in clinical studies to test new treatments.

Masking as a control measure takes place because sometimes, researchers may unintentionally influence the participants to act in ways that support their hypotheses. In another scenario, the goal of the study might be revealed to the participants through the study environment and this may influence their responses.

Masking, however, blinds the participants from having a deeper knowledge of the research whether they’re in the control group or the experimental group. This helps to control and reduce biases from either the researcher or the participants that could influence the results of the study.

• Use of Random Assignment

Random assignment or distribution is used to avoid systematic differences between participants in the experimental group and the control group. This helps to evenly distribute extraneous participant variables, thereby making the comparison between groups valid. Another usefulness of random assignment is that it shows the difference between true experiments from quasi-experiments.

Learn About: Double-Blind Studies in Research: Types, Pros & Cons

How to Design a Controlled Experiment

For a researcher to design a controlled experiment, the researcher will need:

• A hypothesis that can be tested.
• One or more independent variables can be changed or manipulated precisely.
• One or more dependent variables can be accurately measured.

Then, when the researcher is designing the experiment, he or she must decide on:

• How will the variables be manipulated?
• How will control be set up in case of any potential confounding variables?
• How large will the samples or participants included in the study be?
• How will the participants be distributed into treatment levels?

How you design your experimental control is highly significant to your experiment’s external and internal validity.

Controlled Experiment Examples

1. A good example of a controlled group would be an experiment to test the effects of a drug. The sample population would be divided into two, the group receiving the drug would be the experimental group while the group receiving the placebo would be the control group (Note that all the variables such as age, and sex, will be the same).

The only significant difference between the two groups will be the taking of medication. You can determine if the drug is effective or not if the control group and experimental group show similar results. 

2. Let’s take a look at this example too. If a researcher wants to determine the impact of different soil types on the germination period of seeds, the researcher can proceed to set up four different pots. Each of the pots would be filled with a different type of soil and then seeds can be planted on the soil. After which each soil pot will be watered and exposed to sunlight.

The researcher will start to measure how long it took for the seeds to sprout in each of the different soil types. Control measures for this experiment might be to place some seeds in a pot without filling the pot with soil. The reason behind this control measure is to determine that no other factor is responsible for germination except the soil.

Here, the researcher can also control the amount of sun the seeds are exposed to, or how much water they are given. The aim is to eliminate all other variables that can affect how quickly the seeds sprouted. 

Experimental controls are important, but it is also important to note that not all experiments should be controlled and It is still possible to get useful data from experiments that are not controlled.

Explore: 21 Chrome Extensions for Academic Researchers in 2021

Problems with Controlled Experiments

It is true that the best way to test for cause and effect relationships is by conducting controlled experiments. However, controlled experiments also have some challenges. Some of which are:

• Difficulties in controlling all the variables especially when the participants in your research are human participants. It can be impossible to hold all the extra variables constant because all individuals have different experiences that may influence their behaviors.
• Controlled experiments are at risk of low external validity because there’s a limit to how the results from the research can be extrapolated to a very large population .
• Your research may lack relatability to real world experience if they are too controlled and that will make it hard for you to apply your outcomes outside a controlled setting.

Control Group vs an Experimental Group

There is a thin line between the control group and the experimental group. That line is the treatment condition. As we have earlier established, the experimental group is the one that gets the treatment while the control group is the placebo group.

All controlled experiments require control groups because control groups will allow you to compare treatments, and to test if there is no treatment while you compare the result with your experimental treatment.

Therefore, both the experimental group and the control group are required to conduct a controlled experiment

FAQs about Controlled Experiments

• Is the control condition the same as the control group?

The control group is different from the control condition. However, the control condition is administered to the control group. 

• What are positive and negative control in an experiment?

The negative control is the group where no change or response is expected while the positive control is the group that receives the treatment with a certainty of a positive result.

While the controlled experiment is beneficial to eliminate extraneous variables in research and focus on the independent variable only to cause an effect on the dependent variable.

Researchers should be careful so they don’t lose real-life relatability to too controlled experiments and also, not all experiments should be controlled.

Connect to Formplus, Get Started Now - It's Free!

• cofounding variables
• control groups
• controlled experiment
• controlled experiment examples
• controlled experiments
• experimental control
• extraneous variables
• busayo.longe

You may also like:

How to do a Meta Analysis: Methodology, Pros & Cons

In this article, we’ll go through the concept of meta-analysis, what it can be used for, and how you can use it to improve how you...

Descriptive Research Designs: Types, Examples & Methods

Ultimate guide to Descriptive research. Definitions, designs, types, examples and methodology.

Lurking Variables Explained: Types & Examples

In this article, we’ll discuss what a lurking variable means, the several types available, its effects along with some real-life examples

Extraneous Variables Explained: Types & Examples

In this article, we are going to discuss extraneous variables and how they impact research.

Formplus - For Seamless Data Collection

Collect data the right way with a versatile data collection tool. try formplus and transform your work productivity today..

Introduction: Practices, Strategies, and Methodologies of Experimental Control in Historical Perspective

• Open Access
• First Online: 27 February 2024

Cite this chapter

You have full access to this open access chapter

• Jutta Schickore 13  

Part of the book series: Archimedes ((ARIM,volume 71))

1061 Accesses

1 Altmetric

The introduction distinguishes four distinct strands in the history of experimental control. The first is the historical development of control practices to stabilize and standardize experimental conditions. The second is the emergence and career of the comparative design in experimentation, understood as a way of generating and securing knowledge of cause-effect relations. The third involves the unfolding, both in philosophy of science and in the sciences themselves, of methodological discussions on control practices and designs in experimental practice. The fourth is the history of the term “(experimental) control.” The introduction describes how the contributions to this volume address these aspects of experimental control.

You have full access to this open access chapter,  Download chapter PDF

Control is the hallmark of scientific experimentation. If an experiment is deemed to be lacking in control, it is unlikely to gain traction in the scientific community; arguably, an uncontrolled intervention is not even a genuine experiment. Today, scientific articles routinely mention controls and handbooks and instruction manuals on methods in the life sciences call for controlled experiments. Evaluating the appropriateness of controls is a core element of successful peer-review.

But despite its centrality to modern scientific inquiry, many foundational and historical questions about experimental control remain open. Experimental practice has been studied for decades, but only few analyses of scientific control practices in experimentation exist, Footnote 1 with almost nothing written on controlled experimentation in the longue durée . Footnote 2 We know little about changing expectations for well-controlled experiments or about different kinds of control, experimenters’ interpretations of control, or reasons given for applying controls. There is not even consensus about whether experimental control is an ancient, early modern, or Enlightenment concept, or whether it is a more recent feature of scientific inquiry. Footnote 3 This is, in part, because the concepts “control,” “control experiment,” and “controlled experiment” are polysemous, like “replication” or “significance.” In addition, methodological concepts for experimental practice have until recently received comparatively little scholarly attention.

“Control” has been studied mostly as a broader cultural phenomenon in the Western world. Cultural histories of control focus on ideologies and technologies for governing people, procedures, or systems of machines (Levin 2000b ; Derksen 2017 ). Historical studies of control and science have shown how cultural currents, for better or worse, transformed scientific practices into more rigorous endeavors. Historians of science have noted the increasing importance in science of a quantifying spirit (Frängsmyr et al. 1990 ) and the values of precision (Wise 1995 ). They have examined the influence on science of tools such as statistics (Porter 1995 ; Gigerenzer et al. 1989 ) and surveillance devices (Foucault 1975 , 1979 ), as well as bureaucratic procedures such as record-keeping, double bookkeeping, and accounting. These authors have argued that institutional changes in science, such as the rise of the university and urban research laboratories, have helped to standardize scientific practice and make it more exact (Tuchman 1993 ; Dierig 2006 ). Eighteenth-century sciences of state promoted record-keeping, accounting, and statistical assessments of experimental data (Seppel and Tribe 2017 ). Nineteenth- and twentieth-century physics and engineering helped to create automated feedback control mechanisms (Bennett 1993 ), intertwined control and communication systems (Wiener 1948 ), and “networks of power” (Hughes 1983 ). They also brought about catastrophic failure of control, as in failed aerospace missions, plane crashes, and collapsing bridges (Schlager 1995 ). Industrial and technological advancements allowed researchers to engineer the development of living organisms and human heredity (Pauly 1987 ; Paul 1995 ), to standardize living things as model organisms for experiments (Rader 2004 ), and to measure human performance (Rabinbach 1990 ). The twentieth-century nexus of military, industry, and information technologies enabled wide-ranging control over data and information flow (Galison 2010 ; Franklin 2015 ).

Of course, broader socio-political and cultural developments such as industrialization, the institutionalization of university research laboratories, and the expansion of bureaucracies and state administration are impactful. These developments change how practices of research, recording, and record-keeping are organized, as so many authors have demonstrated. But they do not fully determine experimental designs or experimenters’ views on what is considered good and well-controlled or deficient and poorly controlled experimental practice.

This volume shifts the focus from broader socio-political and cultural contexts of control onto practitioners’ methodological strategies of inquiry and experimental design. While acknowledging that broader cultural forces do affect control practices, we contend that these forces only partially shape experimental design and strategy. We identify additional social dimensions of experimental control. On the one hand, identifying experimental conditions, confounders, and solutions to technical problems in experimental design takes time, and unfolds by the activities of multiple individuals or groups. On the other hand, whether an experiment counts as “sufficiently” or even “fully” controlled is not entirely decided by the experimenters themselves, nor can the question be settled by comparing actual experimentation with an abstract standard of the ideal controlled experiment. Footnote 4 The adequacy of control critically depends on the social interactions and negotiations among experimenters and their various interlocutors; as such, the issue is open to revisiting, revision, and renegotiation.

To capture the complicated and multilayered history of experimental control, it is useful to distinguish control strategies, control practices, and methodological ideas about experimental control. Control strategies are general designs and plans to follow in an experiment, like the comparison of an intervention target with a control. Control practices are the concrete actions by which experimenters implement control strategies in particular contexts. These contexts comprise all the resources available to the experimenters, including materials, tools, techniques, local expertise, and institutional opportunities. Methodological ideas are the broader notions of how to study nature and everything in it. They are contained in accounts of control strategies and practices, as the practitioners themselves give them. Footnote 5

Contributions to this volume deal with the details of experimental control practices, as well as with the expectations and perceived obstacles for experimental designs. The chapters are also sensitive to long-term developments of control strategies and methodological ideas. We provide a set of focused studies on control practices, strategies, and ideas that, together, cover a period of more than 300 years, with glimpses back to antiquity and forward to the late twentieth century. We contend that the long-term perspective is productive for understanding experimental methodologies and experimental control in particular. Footnote 6 The chapters offer several examples of how control practices using those strategies and ideas are shaped by local contexts—material-technical, conceptual, and social. Together, they illustrate that control strategies and methodological ideas often remain stable for a long time and change only gradually.

To study controlled experimentation from a historical perspective, we must distinguish at least two notions of control. The first is a broad sense of control as “managing,” “restraining,” or “keeping everything stable except the target system to be intervened upon.” This notion primarily but not exclusively concerns the experiment’s material side—the objects, the setting and environment, and the tools, as well as the guided manipulation or intentional intervention in an otherwise stable situation to see what will happen. Footnote 7

In an uncontrolled situation, experimenters cannot determine the changes resulting from their interventions. To extract information from unwieldy experimental situations, they must standardize instruments and experimental targets and hold fixed the experimental background conditions. They ought also to be free of preconceived opinions and other sources of influence. Experimenters seek to make the experimental setting and background as stable and rigorous as possible because effects, both expected and novel, appear most distinctly against a stable background. Footnote 8 Generally, then, we can consider any aspect of experimental practice from the perspective of control; a key question is how experimenters identify what must be controlled in concrete contexts and how they achieve that control.

There is also a narrower notion of control, referring to comparative experimental designs. Footnote 9 It primarily but not exclusively concerns the experiment’s epistemic side, or the conditions required for the experiment to generate knowledge. Modern scientists typically associate with “control experiment” a particular experimental strategy or design, namely the comparison to a control case. An experimental intervention is compared with a baseline; the target system of the intervention is compared with a similar target system that, unlike the experimental object, was not intervened on (the “control mouse,” say, which did not receive treatment). This strategy encapsulates the requirements for an experiment to be informative about cause-effect relations. Footnote 10

In the narrow sense, comparison to a baseline is needed to find out whether it really was the manipulation of this particular variable that made a difference to the experimental outcome. Footnote 11 Of course, the more similar the experimental situations are, the more informative the comparisons will be. Making informative comparisons thus requires control practices in the broader sense explained above, to ensure that the two experimental settings are stable, save the intervention.

We should avoid confusing the emergence of terms such as “control experiment” and “experimental control” in the scientific literature with the emergence of explicit discussions about control practices and strategies. The terms “control experiment,” “controlled experiment,” and “experimental control” are recent terms. Google Ngram shows a steep increase for “control experiment” in the last decades of the nineteenth century in English, French, and German-language scientific literature. Of course, Ngram is not a rigorous tracker for word usage, but based on its data, we can safely assume that control practices were common long before the term spread in scientific writing. Footnote 12 As our volume demonstrates, discussions about stable experiments antedate the appearance of the term “control” in this literature. Concerns about the adequate management of experimental settings were voiced as soon as experimentation became widespread. Robert Boyle, for one, published two famous essays on “unsucceeding” experiments, where he discussed the obstacles posed by impure chemicals, the variability of body parts in different corpses, and other issues threatening experimental success (Boyle 1999a , b ).

The history of experimental control, then, encompasses four distinct yet related strands. The first is the historical development of control practices to stabilize and standardize experimental conditions. The second is the emergence and career of the comparative design in experimentation, understood as a way of generating and securing knowledge of cause-effect relations. The third involves the unfolding, both in philosophy of science and in the sciences themselves, of methodological discussions on control practices and designs in experimental practice. The fourth is the history of the term “(experimental) control.”

This volume concerns itself most with the first three strands. We do not systematically explore the history of the term “control;” Footnote 13 in fact, several contributions discuss research from before the late nineteenth century. However, precisely because control practices and strategies predate the term “control” in scientific literature, we keep terminological questions in mind as we analyze past experimental reports and methodological discussions. We pay careful attention to the terms past practitioners did use, whatever they were, to describe, explain, and defend control practices and strategies.

The contributions here examine how control practices and comparative designs developed, and include past accounts of critiques and defenses for these practices. Control is a multifaceted and elusive concept, and our volume reflects this. We have not attempted to reduce our discussion to a single definition of “control.” Although this introduction provides some points of orientation for analyzing control practices and strategies, each contributor further explains the concept for specific experimental contexts. The chapters range over different fields, from botany and vision studies, ecology and plant physiology, human physiology and psychology to animal behavior and experimental physics. They cover a period from the early seventeenth to the twentieth century. They examine experiments with complex and sometimes unwieldy objects and elusive phenomena. Chapters deal with studies on learning and judgment; color blindness in animals; auditory perceptions of tones, pitch, and vowel sounds; irregular movements; psychic forces; unobservable elements; and the best “photogenic climate” for promoting photosynthesis. Experiments on such objects and phenomena are hard to design, stabilize, and carry out, and they are often controversial. For this reason, they showcase questions and reflections on control in science particularly well.

The very practice of creating and maintaining a stable experimental situation is old, arguably as old as experimental intervention itself. Over time, experimenters learn what must be managed and tracked in experimental contexts; they seek to localize the phenomena of interest as well as the elements of the experimental setting in order to make interventions more exact. Gradually they develop new tools to do this. Precision instruments, elaborate recording devices, and other technologies available in the last century or two can assist with these tasks. The history of research laboratories can be written as the history of efforts to create highly controlled research environments. Nineteenth-century physicists worked at night or retreated to the lab basement to escape city noise, vibrations from trams, and exuberant students (Hoffmann 2001 ). Today’s scientists turn to specialized construction companies when they need “clean rooms” for research. Footnote 14 All-metal or all-plastic labs are built for research into the impacts of micro-plastics on materials and tissues or on radiation, respectively. Particle physicists dive to recover radiation-free lead from ancient shipwrecks to prevent contaminating their measurements.

Such materials and technologies often make it easier to keep an experimental situation stable and to track interesting changes. Footnote 15 At the same time, however, closer analysis of actual episodes shows that advancements in instrumentation, impressive as they may appear in hindsight, do not guarantee improved control. In fact, obtaining control often becomes more difficult, not least because researchers must learn the instruments’ proper functioning. “The more finely a method of investigation operates, the more complicated the devices used must be,” as Carl Stumpf noted (1926, 8). Footnote 16

Moreover, the history of control is a history of efforts—and efforts can fail. Implementing control strategies often fails, as even the experimenters themselves sometimes admit. Our volume illustrates how difficult it can be to manage an experimental setting, how resourceful some experimenters were in their management, and how they sometimes failed to achieve it despite intense effort. Claudia Cristalli’s researchers of psychic phenomena walk the line between controlling the psychic powers of the “percipients” in their experiments, and preventing them from sensing any phantasms at all. Christoph Hoffmann’s study of color blindness in fish shows how experimenters dealt with the tricky problem of controlling animals’ behavior. Experimenters found different solutions, both difficult to implement and neither completely satisfying. One option was to train the fish—much more challenging to do than training, say, a dog or rat. The other was to design the experimental setting in such a way that the “normal” behavior of the fish was taken into account when the behavior of interest was elicited. But what is the “normal” behavior of fish? And how can it be accommodated in the unnatural environment of a laboratory fish tank?

Other contributions illustrate how experimenters approached the creation and monitoring of an experimental setting. They discuss the multifaceted nature of the associated problems and the obstacles the experimenters had to overcome when attempting to stabilize unwieldy things, such as the irregular movements of microscopic parts, the germination, sprouting, and growth of plants, and auditory perceptions. The contributions describe the solutions they found to these problems. Experimenters tried their best to identify the smallest details of the experimental settings deemed relevant, and sometimes invented remarkably elaborate contraptions to keep them stable.

Caterina Schürch depicts the curious machines with which eighteenth-century plant physiologists tried to electrify plants and seeds with precise doses of electricity. Kärin Nickelsen shows how the nineteenth-century plant physiologist Julius Wiesner designed an artificial environment for his plants: double-walled glass jars, with the space between the walls filled with a solution of iodine in carbon disulphide. Because this liquid layer absorbed all visible light but heat rays, Wiesner could examine the impact of those rays on plant growth. Julia Kursell describes the giant arrangement of tubes Carl Stumpf erected to compare how his experimental subjects perceived natural and machine-generated vowels. She notes that, according to Stumpf, the increased finesse of experimental tasks required ever more complex experimental devices. Cristalli shows how Faraday, attempting to stop participants in table-turning experiments from making involuntary movements, designed a device consisting of a stack of cardboard sheets, arranged like a voltaic pile, with pellets of wax in between. The device would be placed between the hands of the séance participants and the tabletop. The sheets were arranged and marked in such a way that their displacement would indicate hand movements prior to the table’s movement.

These devices often astonish with their ingenuity, but the point is that they are the material realizations of what experimenters recognized as the relevant conditions and potential confounders for their experiments. They are therefore purpose-dependent, as Kursell notes; at the same time, they both constitute and constrain the generation of experimental knowledge. Cristalli’s, Schürch’s, Nickelsen’s and Evan Arnet’s chapters demonstrate this constraint: over time, views about what factors to manipulate, keep fixed, or monitor in controlled experiments might change considerably, even within a single research tradition. While Faraday built tools to control his subjects’ involuntary movements, his American colleague and erstwhile admirer Robert Hare turned to designing machines that would prevent voluntary movements in psychic experiments—in other words, to prevent fraud.

Schürch’s account illustrates a most dramatic change of focus. After decades of carefully controlled experimentation, which supported the view that electrification promotes plant growth, Jean Ingen-Houz showed, using the same control strategies, that it was not electricity but differences in light intensities that affected the plants. He thus re-oriented the entire research program of plant growth, rendering previously “well-controlled” experiments uncontrolled.

Similarly, in maze research on animal learning, later investigators critiqued their predecessors for stabilizing—“controlling for”—the very phenomenon they should have studied, as Arnet’s work illustrates. Nickelsen shows how control practices in photosynthesis research changed fundamentally as the experiments moved from the laboratory to the field. As she observes, the changes were not just practical—measuring natural light is harder than measuring laboratory light—but also conceptual. What mattered was no longer just “daylight,” but a complex set of factors consisting of the specific light individual plant parts received, intensity fluctuations during the day and the season, and so forth. Klodian Coko charts another kind of reorientation in his study of research on Brownian movement. Using the strategy of comparative experimentation, nineteenth-century researchers tried to establish what could and could not be the cause of Brownian movement. Later in the century, Brownian movement itself became evidence for a new kinematic-molecular theory of matter, which changed the understanding of rigor and experimentation.

Several chapters also direct attention to the fact that many experimenters were explicitly concerned with developing coping strategies for “limited beings” (Wimsatt 2007 ) in sub-optimal situations. Researchers faced challenges not only because background factors were difficult or too numerous to monitor, but also because those factors were not immediately observable. Remarkably, the physicist Lord Rayleigh devoted several of his public-facing remarks to the theme of “deficient rigor.” As Vasiliki Christopoulou and Theodore Arabatzis point out, for Rayleigh, the pursuit of absolute (“mathematical”) rigor could even be detrimental to progress in physics. It was in this situation that experimenters insisted on using two or more different experimental techniques to check if both converged on the same outcomes, as detailed in the contributions by Christopoulou and Arabatzis and by Coko.

Notably, experimenters developed strategies to guard against entirely unknown influences on their experiments. The notion that natural phenomena in an experiment might occur and not occur in unforeseeable ways is centuries old. The metaphysical interpretation of this notion has changed dramatically over time (Hacking 1984 , 1990 ), but there was wide and long-standing agreement about how to address it: namely, through multiple repetitions of experimental trials. Both the early seventeenth-century experimenter Scheiner and the late nineteenth-century experimenter Rayleigh gave the idea of multiple repetitions an important role in rigorous experimentation, if for different reasons.

In an early essay on medical experience, the ancient physician and anatomist Galen discussed the possibility that what is seen only once in a patient may not be a regular occurrence, and thus may not be worthy of acceptance and belief. Galen suggested this point in the middle of his attempt to demonstrate that medical practice is not just logos , but also experience. Footnote 17 As part of the argument, Galen alluded to the instability of memory and also noted that medicines work sometimes but not always (Galen 1944 ). In clinical medicine, at least, one single drug test might not produce reliable results, because “some things are frequent and some are rare” (Galen 1944 , 113). It must therefore be repeated several times, and even then, it may not tell us what is usually the case. Footnote 18 Ibn Sīnā (Avicenna) expressed a similar idea in a proposal for rules of drug testing, albeit with a positive spin. He wrote that “the effect of the drug should be the same in all cases or, at least, in most. If that is not the case, the effect is then accidental, because things that occur naturally are always or mostly consistent” (Nasser et al. 2009 , 80).

In the early modern period, we encounter this idea frequently, now also in discussions about experimentation beyond drug testing in clinical medicine. Repeating experimental trials several times, indeed “very many times,” became an imperative for rigorous experimentation—in this way, unknown or contingent and accidental influences on experiments could be avoided. Footnote 19 In later centuries it was to become a hallmark of rigorous experimentation that a trial be done more than once or on large samples. Footnote 20 However, as Schürch’s chapter shows, the appropriate number of repetitions remained contested.

Scholars looking for the “first” control experiment in the history of scientific inquiry typically assume, but in most cases tacitly, the narrower notion of “control” as comparative trial. They have found quite early examples for comparative designs in experimental practice. These examples often come from medicine, where it is both vitally and commercially important to discover the efficacy of certain drugs and treatments. The reputation of a practitioner depended on the treatments’ success.

For example, historian of statistics Stephen Stigler finds an instance of comparative experimentation in the Old Testament, in the Book of Daniel (around 164 BCE). Servants on a vegetarian diet are compared with children who eat “the king’s meat”: “And at the end of ten days their countenances appeared fairer and fatter in flesh than all the children which did eat the portion of the king’s meat” (Daniel 1:5–16). Footnote 21

A passage by Athenaeus (200 CE) describes how some convicted criminals had been thrown among asps and survived. It turned out that they had been given lemons prior to their punishment. The next day a piece of lemon was given to one convict but not to another. The one who ate the lemon survived the bites, the other died instantly. Footnote 22 The pseudo-Galenic treatise on theriac describes a trial with a similar design, whereby two birds would be poisoned and only one given an antidote (Leigh 2013 ). The trial tests the efficacy of medicines: if both animals survived, the tested antidote was recognized to be ineffectual. That experiment was again reported in the Middle Ages, notably by Bernard Gordon (McVaugh 2009 ).

Another famous ancient example is the legend of Pythagoras. As the story goes, he observed that most combinations of blacksmiths’ hammers generated a harmonious sound when striking anvils at the same time, while some did not. Pythagoras discovered that harmonious sounds were produced by those hammers whose masses were simple ratios of each other, while other hammers made dissonant noises when struck simultaneously. Notably, Ptolemy later criticized the Pythagorean experiment because, to him, it lacked control (Zhmud 2012 , 307).

The Pythagorean case is interesting. It clearly has a comparative component, inspecting the sound of hammers whose masses were simple ratios of each other and that of other hammers. But in the historiography of science it does not serve as an example of an early “control experiment.” In fact, the ancient texts have too little information to determine whether it was consciously performed as an experiment compared with a control, whether Pythagoras simply varied the setup, or whether he arrived at his conclusions by observing different blacksmiths at work.

Conscious and explicit implementation of comparative designs appears to become more common in seventeenth- and eighteenth-century experimental practice. In his studies on the generation of insects, Francesco Redi famously compared samples of organic materials—“a snake, some fish, some eels of the Arno, and a slice of milk-fed veal in four large, wide-mouthed flasks” (Redi 1909 , 33)—kept in open and closed containers. The samples were periodically inspected for traces of life. No life developed in closed containers, which Redi took as evidence against the spontaneous generation of maggots from putrefying flesh. Here, the comparative design demonstrates a cause-effect relation through the comparison with a “control.” Redi showed that maggots in open containers were generated by flies’ eggs. Footnote 23

The case of spontaneous generation research illustrates particularly well why it is useful to distinguish between comparative design strategies and a broader notion of control as management of the experimental setting. Redi’s experimental research was not decisive, and after him many other experimenters investigated spontaneous generation. They all contested each other’s experiments and many argued that their opponents had not properly maintained the experimental settings; they also argued that they themselves really had taken the necessary precautions to do so. John T. Needham, for instance, claimed that he could demonstrate the spontaneous generation of animalcules in infusions. He told his readers that he had “neglected no Precaution, even as far as to heat violently in hot Allies the Body of the Phial; that if any thing existed, even in that little Portion of Air which filled up the Neck, it might be destroy’d, and lose its productive Faculty” (Needham 1748 , 638). Notably, he did not report a comparison with a vial that had not been heated in fire. It may have been superfluous to him, because it was obvious that animalcules would appear in it, as so often had been observed. The debates continued throughout the nineteenth century. Experimental designs and interpretations for possible contaminants varied, but the comparative strategy generally remained the strategy of choice. Footnote 24 As Schürch’s contribution shows, in the decades around 1800, experimenters across Western Europe advocated comparative experimental designs.

Reports of comparative trials can be found in many fields, from agriculture to clinical medicine. Footnote 25 A notable but little-studied example is steeping experiments (Pastorino 2022 ). A comparative experiment by Francis Bacon served as a template for many subsequent experiments on the effects of plant growth when steeping seeds in various fluids.

Our volume illustrates comparative trial designs in plant physiology, physics, animal behavior studies, and psychology. The episodes exemplify both the conscientious application of these strategies and the obstacles experimenters faced as they attempted to realize well-controlled comparative trials.

The earliest pre-modern reports of experimental trials and comparative designs contain little express discussion on control practices and strategies. There are exceptions, of course, especially in medical contexts. I already noted Galen’s writings, and we know that medieval scholars such as Ibn Sīnā developed rules for drug testing (Crombie 1952). Mostly, however, comparative designs were simply described and rarely justified; there was little explicit concern with managing the details of experimental settings. When ancient and medieval authors noted the drug test on two birds, they surely meant to show a test to support the drug’s efficacy, but the argument for the comparative approach often remained implicit. In modern scientific writing, by contrast, we sometimes find detailed discussions and justifications of experimental designs—in controversies about experimental results, in debates about the status of heterodox scientific fields such as research on psychic phenomena, and in situations of uncertainty.

In this volume, Tawrin Baker’s chapter on Scheiner and Christopoulou and Arabatzis’s chapter on Rayleigh epitomize both the scarcity and the abundance of practitioners’ discourse on their control practices and strategies. Scheiner demonstrated to his readers how experimentation could serve as a legitimate check on a theory of vision. He did not expound or defend methodological ideas in detail, although he did focus attention on the process of experimentation. Words and pictures conveyed the experimental setups. Scheiner instructed his readers to make certain experiences and experiments; he discussed the implications for the theory of vision. However, as Baker notes, several issues remained open, such as how often an experiment should be repeated or how one ought to deal with discrepancies. Christopoulou and Arabatzis’s chapter on Rayleigh shows that late-nineteenth-century scientists wrote not only about the details of their experiments but also about experimental control. Experimenters drew attention to how they had re-designed instruments to make their measurements more precise and how they had employed additional instruments to check the quality of their measurements. They often insisted on using two measurement methods to guard against error.

We still know little about the unfolding of methodological discussions in the centuries after Scheiner’s appeal to a variety of experiences and experiments and Boyle’s musings on unwieldy, “uncontrolled” experimental settings and about the practices appropriate for managing and extracting knowledge from these settings. Little is known about the emergence of explicit methodologies for comparative trials. According to some scholars, notably Edwin Boring, it was not until the mid-nineteenth century that we find such explicit methodologies. Boring associated the first methodology of comparative experimental designs with a philosophical text, John Stuart Mill’s System of Logic (Boring 1954 ) . While the contributions to our volume do not tell a comprehensive history of methodological accounts on experimental control, they do suggest that it would be misleading to identify Mill as the sole originator and principal representative of these accounts. Footnote 26 As Schürch’s, Coko’s and Nickelsen’s chapters demonstrate, Mill was one of several early-nineteenth-century commentators on science who urged investigators to keep background conditions constant across trials, to “analyze” the background into different experimental conditions, and to compare the effects of interventions in one setting to another setting left untouched. But a broader history of these developments would still be desirable.

Our volume also shows that reflections about and justifications of control strategies predate modern philosophies of science. From Schürch’s study of late-eighteenth-century plant physiology we learn that, prior to Mill, practitioners not only called for rigorous and properly managed interventions, but also did much more: they reflected on control practices as validation procedures and debated their relative merits, practicality, and limitations. They observed that, to be instructive, comparisons must be made on sufficiently similar experimental subjects in similar situations. At times they disagreed about whether they or their colleagues had done enough to control their experiments. They criticized each other for not making comparative trials, for not controlling the right thing, or for not repeating a trial often enough.

The content of these debates and reflections tells us something about the experimenters’ own understanding of methodological issues concerning control, rigor, reliability, certainty, and failure in experimentation. Christopoulou and Arabatzis’s and Coko’s chapters illustrate this. As many contributors show, satisfactory control of an experiment is, in the end, an intersubjective, iterative achievement. Schürch and Christopoulou and Arabatzis note that experimenters such as Ingen-Housz and Rayleigh call upon others to check the results they themselves had obtained and to contribute additional experiments. Footnote 27 Cristalli charts the decades-long negotiations and re-negotiations among physicists, chemists, and psychologists on experimental practices deemed adequate to study psychic phenomena. The experimenters understood that their projects’ success depended on “controlling” their interlocutors as well. Footnote 28

This volume does not aim to replace earlier systematic discussions in history and philosophy of science on these issues, such as those on epistemological strategies of experimentation (Allan Franklin), tests for error (Deborah Mayo), representing and intervening (Ian Hacking), and how experiments end (Peter Galison). Our volume complements them. In fact, our discussions overlap with these approaches as we trace the history of controls while keeping epistemological strategies of experimentation in mind. We do contend that re-directing attention to control practices, control strategies, and practitioners’ accounts thereof illuminates new aspects of the history of experimental practices.

Control strategies and practices can be viewed as long-term and short-term methodological commitments, along the lines suggested by Peter Galison ( 1987 ). Arnet’s contribution to this volume uses this approach. Material and conceptual organizations of experiments vary, as do the identification of target systems, conditions, and confounders. The tools for stabilizing them change as well and are often (but by no means always!) local, context-specific, and relatively short-lived. Modern technologies allow for creative and sometimes intricate solutions to the problems of stabilization, standardization, and tracking. Yet the strategies have long been in place.

Control strategies are persistent. Even in the most complicated settings and with the most elusive phenomena, experimenters try to implement established control strategies as best they can, as shown in Schürch’s study of plant electrification, Coko’s discussion of experiments on Brownian movement, Cristalli’s study of psychic experiments, Kursell’s work on elusive auditory judgments, and Nickelsen’s discussion of plant physiology. Experimenters look for experimental conditions and confounding factors; they vary them to weigh their influence on experimental processes; they probe for error (Mayo 1996 ); they make their interventions less “fat-handed” (Woodward 2008 ); they compare situations meant to be similar and assess robustness, presupposing the no-miracle argument (Hacking 1985 ). At the same time, they develop specific, contextual implementations for these strategies, and they do not always agree on whether a particular implementation is effective.

In doing all this, experimenters face both technical and conceptual challenges. It may take a long time to harness experimental conditions, identify potential confounders, and find suitable techniques for doing so. Solutions to control problems will typically remain less than ideal. Hoffmann’s contribution demonstrates this fragility in control procedures. In debates about spontaneous generation, it took centuries to refine the tools to prevent contaminations from reaching the materials under investigation, and every new tool generated new issues for further exploration. Along the way, the understanding changed regarding the causes, conditions, and potential modifying factors and confounders. New technical challenges arose as a result.

Several chapters show that the implementation of control strategies may generate entirely new technical and conceptual problems for the experimenter, or even produce “surplus findings,” as Kursell writes. Footnote 29 Nickelsen, for instance, tracks changes in both the conceptualization and the logistics of managing background conditions for experiments on the influence of light on plant growth. Christopoulou and Arabatzis suggest that disturbances in physics experiments could become research topics in their own right. Arnet’s work also brings into relief the problematic implications of an over-emphasis on rigor and control. Early mazes were designed as simple systems of tracks in order to minimize environmental cues. But for a more complete understanding of animal learning, later researchers re-introduced precisely those same environmental features. The early mazes embodied a regime of control that stripped animals of certain sensory and environmental cues. Those mazes, however, excluded exactly those features that later researchers thought essential to advanced rodent learning. Footnote 30

Finally, several chapters suggest that it is fruitful to think of experiments as “controls of inferences,” because this perspective also brings out relevant methodological issues and their historical development. As Baker demonstrates, for early modern experimenters coming to grips with their Aristotelian heritage, the role of experiments in scientific inquiry was a crucial issue. In hindsight, studying how they managed this issue can also tell us something about Aristotle’s own ideas on the role of experimentation in empirical inquiry. For eighteenth- and nineteenth-century inquirers, then, the question is not so much whether but how, exactly, experimentation and experimentally generated knowledge can help us to understand nature. Steinle, Coko, Nickelsen, Kursell, and Hoffmann show how intricate the question can be as experiments target unobservable phenomena. As these experiments involve increasingly complicated instruments, hypotheses, assumptions, chains of inferences, and interpretations, the challenges for experimenters increase accordingly.

We place practitioners’ methodologies, experimental designs, strategies of inquiry, and practices of implementation in the center of our analyses. We thereby draw new trajectories and connections in the history of experimental inquiry. We identify lines of experimentation that sometimes turned into models of rigorous experimental design while other times being criticized. Bacon’s steeping experiments with plant seeds, as analyzed by Pastorino, exemplify a specific kind of comparative experimentation. It would be applied again and again throughout the eighteenth century, not just in plant science but also in other scientific fields. Pythagoras’ hammer experiments too were repeated, at least repeatedly reported, by several scholars prior to Galileo and Mersenne. In this case, the design was not a model but a point of critique for later scholars.

Our studies on control practices and on their discussion and justification have revealed other lineages and cross-fertilizations—among physics and psychology, physiology, botany and ethology, chemistry, medicine, agriculture, and philosophy. Control practices and strategies are contextual, in that the context determines what is controlled and how to achieve control. But control strategies and at times even control practices are not discipline-specific. The same strategies travel across disciplines, from physics to medicine and physiology to chemistry and back again. Several chapters suggest that the same methodological ideas and control strategies are advocated across national boundaries (see especially Schürch and Coko). Control strategies such as comparative designs and multiple repetitions are relatively stable across historical periods. But they may be justified in different ways at different times and may cease to be justified at all.

With our work, we hope to stimulate broader discussions about the longer-term history of rigorous experimentation: what are the strategies involved in it? And how do debates concerning well-designed experiments unfold in different fields and periods? By our effort we seek to clarify the roles of experimental strategies and methodologies as driving forces for scientific change, and as tools for determining what it means to do—or not to do—good science.

This volume (and its companion, a collection of essays on analysis and synthesis) originated in a Sawyer Seminar at Indiana University Bloomington titled “Rigor: Control, Analysis and Synthesis in Historical and Systematic Perspectives,” which was funded by the Andrew W. Mellon Foundation. Mellon Sawyer Seminars are temporary research centers, gathering together faculty, postdoctoral fellows, and graduate students for in-depth study of a scholarly subject in reading groups, seminars, and workshops. As part of our activities, we organized two international conferences. They brought together scholars in history, philosophy, and social studies of science who examine historical and contemporary dimensions of rigor in experimental practice. The contributors to this volume participated in the second of the Sawyer conferences (March 2022) and reconvened a few months later for an authors’ workshop, at which the draft chapters for this volume were intensely discussed.

Several institutions and individuals helped to make our work possible. We gratefully acknowledge the Mellon Foundation’s generous financial support, and especially the Foundation’s flexibility as we dealt with the challenges of pursuing collaborative scholarship during a pandemic. We are grateful to Director of Foundation Relations Cory Rutz at Indiana University’s Office of the Vice President for Research, for his prompt and efficient assistance in administering the grant. The authors’ workshop took place at the IU Europe Gateway (Berlin) and was funded by a combined grant from the IU College of Arts and Sciences and the College Arts and Humanities Institute. We very much appreciate this support. We are indebted to Jed Buchwald for including our work in the Archimedes series, and to Chris Wilby for his efforts in moving the publication along. A big thank you to our department manager Dana Berg (Department of History and Philosophy of Science and Medicine at IU), office assistant Maggie Herms (IU HPSC), and Andrea Adam Moore (IU Europe Gateway), all of whom helped to organize our conferences and workshops. Finally, we warmly thank the many participants at the two conferences and at the various other Sawyer events for their valuable input, comments, questions, and critique.

This is slowly changing, see Guettinger ( 2019 ); Sullivan ( 2022 ); Guettinger ( 2019 ); Desjardins et al. ( 2023 ).

Only the randomized controlled trial has been studied historically and systematically. See Marks ( 1997 , Chap. 5); Worrall ( 2007 ); Cartwright ( 2007 ); Keating and Cambrosio ( 2012 ). For the control group and (double) blind tests, see Kaptchuk ( 1998 ); Strong and Frederick ( 1999 , including further references); Dehue ( 2005 ); Holman ( 2020 ).

For a variety of views, see, for instance, McCartney ( 1942 ); Beniger ( 1986 ); Levin ( 2000a , 13–14); Amici ( 2001 ).

Two classic studies of how experimenters sought to “control” their audiences are Shapin and Schaffer ( 1985 ) and Geison ( 1995 , especially Chap. 5).

These ideas are also articulated in the philosophy of science, of course. In this volume, however, we are concerned mostly with practicing experimenters’ working philosophies.

Some historians have strong reservations about long-term histories “lining up unconnected look-alikes through the ages” (Dehue 2005 , 2), or “ahistorical narratives” comparing, for instance, early modern and Victorian experiments “merely because of superficial similarity ‘in the use of controls’” (Strick 2000 , 5, commenting on spontaneous generation experiments). Our volume shows that it is possible to write long-term histories without comparing apples to oranges.

These distinctions are inspired by one of the few systematic studies of controlled experimentation, Edwin Boring’s “The Nature and History of Experimental Control” (Boring 1954 ).

This insight underlies Ludwik Fleck’s and Thomas Kuhn’s accounts of scientific change.

Comparison, Boring noted, “appears in all experimentation because a discoverable fact is a difference or a relation, and a discovered datum has significance only as it is related to a frame of reference, to a relatum” (Boring 1954 , 589).

For the epistemic ideal underlying this design, the “perfectly controlled experiment,” see Guala ( 2005 , 65–69).

I keep this characterization vague because I do not want to commit to a specific philosophical understanding of causality here.

Technical terms such as “positive” and “negative” control are even more recent (and outside the timeframe of our volume). They are also poorly understood.

For a brief overview of historical definitions of control, see Levin ( 2000a , 21–31).

See Holbrook ( 2009 ).

See, e.g., Kuch et al. ( 2020 ).

The quotation is drawn from Kursell’s chapter in this volume.

Much of the text rebuts the sorites argument, according to which it is impossible to clarify the notion of seeing something “very many times” (see Galen 1944 , 124–25). For a reconstruction of the argument, see (Kupreeva 2022 ).

For the Aristotelian notion of the memory of many instances, see Bayer ( 1997 ). For its application in the scholastic-mathematical tradition, see Dear ( 1991 ).

On repetition and “many, many” trials, see Schickore ( 2017 , chapters 1–3).

A popular passage by Karl Popper expresses this idea: “Every experimental physicist knows those surprising and inexplicable apparent ‘effects’ which in his laboratory can perhaps even be reproduced for some time, but which finally disappear without trace. Of course, no physicist would say in such a case that he had made a scientific discovery (though he might try to rearrange his experiments so as to make the effect reproducible). Indeed the scientifically significant physical effect may be defined as that which can be regularly reproduced by anyone who carries out the appropriate experiment in the way prescribed. No serious physicist would offer for publication, as a scientific discovery, any such ‘occult effect,’ as I propose to call it—one for whose reproduction he could give no instructions. The ‘discovery’ would be only too soon rejected as chimerical, simply because attempts to test it would lead to negative results. (It follows that any controversy over the question whether events which are in principle unrepeatable and unique ever do occur cannot be decided by science: it would be a metaphysical controversy)” (Popper 2002 , 23–24).

This example is also quoted on the website of the Institute for Creation Research as a model for sound experimental design (Treece 1990 ).

Deipnosophists or Banquet of the Learned, 3.84 d-f:2. The reference is from McCartney ( 1942 , 5–6).

For details on Redi’s experiments, see Parke ( 2014 ). Historians of biology as well as science educators regularly cite Redi’s experiments on spontaneous generation as “the first control experiments.”

In his well-known book on Pasteur, Gerald Geison drew on Pasteur’s experiments with infusions to show that the negotiations of what does and does not count as a properly controlled experiment in the spontaneous generation debates turned into battles motivated by political and religious concerns. Geison argues that Pasteur effectively “controlled” his audiences (Geison 1995 ).

Bertoloni Meli ( 2009 ) describes many other comparative experiments from the early modern period. See also Schickore ( 2021 ).

Our volume focuses on practitioners’ methodological accounts. However, even in philosophy of science, Mill had predecessors in this regard: Dugald Stewart and John Herschel, for instance, cover territory very similar to Mill’s four methods of experimental inquiry.

For another example of appeals to the community in the struggle to identify the causes of blue milk, see Schickore ( 2023 , 37).

See Schürch’s discussion of Ingen-Housz in this volume, for example.

For another example of how control practices themselves become the object of study, see Landecker ( 2016 ).

Researchers today have identified other areas of concern for over-emphasizing rigor and control. One example is over-standardized mice (Engber 2013 ), and these studies highlight the importance of balancing control with other demands on research design. In public health studies, researchers must overcome barriers for recruitment, attrition, and sample size, which may necessitate lowering the bar for rigor to gather any valuable information at all (Crosby et al. 2010 ). Thus, the implication of an over-emphasis on rigor may be epistemic, socio-political, or both.

Amici, Raffaele Roncalli. 2001. The History of Italian Parasitology. Veterinary Parasitology 98: 3–30.

Article   Google Scholar  

Bayer, Greg. 1997. Coming to Know Principles in Posterior Analytics II 19. Apeiron 30: 109–142.

Beniger, James. 1986. The Control Revolution. Technology and Economic Origins of the Information Society . Cambridge, MA: Harvard University Press.

Google Scholar  

Bennett, S. 1993. A History of Control Engineering . Wiltshire: Redwood Books.

Bertoloni Meli, Domenico. 2009. A Lofty Mountain, Putrefying Flesh, Styptic Water, and Germinating Seeds. In The Accademia del Cimento and its European Context , ed. Marco Beretta, Antonio Clericuzio, and Larry Principe, 121–134. Sagamore Beach: Science History Publications.

Boring, Edwin Garrigues. 1954. The Nature and History of Experimental Control. American Journal of Psychology 67: 573–589.

Article   CAS   PubMed   Google Scholar  

Boyle, Robert. 1999a. The First Essay, of the Unsuccessfulness of Experiments. In The Works of Robert Boyle , ed. Michael Hunter and Edward B. Davies, 37–56. London: Pickering & Chatto. Original edition, 1661.

———. 1999b. The Second Essay, of Unsucceeding Experiments. In The Works of Robert Boyle , ed. Michael Hunter and Edward B. Davies, 57–82. London: Pickering & Chatto. Original edition, 1661.

Cartwright, Nancy. 2007. Are RCTs the Gold Standard? BioSocieties 2: 11–20.

Crosby, Richard, Laura F. Salazar, Ralph DiClemente, and Delia Lang. 2010. Balancing Rigor Against the Inherent Limitations of Investigating Hard-to-Reach Populations. Health Education Research 25: 1–5.

Article   PubMed   Google Scholar  

Dear, Peter. 1991. Narratives, Anecdotes, and Experiments: Turning Experience into Science in the Seventeenth Century. In The Literary Structure of Scientific Argument: Historical Studies , ed. Peter Dear, 135–163. Philadelphia: University of Pennsylvania Press.

Chapter   Google Scholar  

Dehue, Trudy. 2005. History of the Control Group. In Encyclopedia of Statistics in Behavioral Science , ed. Brian S. Everitt and David C. Howell, 829–836. Chichester: Wiley.

Derksen, Maarten. 2017. Histories of Human Engineering: Tact and Technology . Cambridge: Cambridge University Press.

Book   Google Scholar  

Desjardins, Eric, Derek Oswick, and Craig W. Fox. 2023. On the Ambivalence of Control in Experimental Investigation of Historically Contingent Processes. Journal of the Philosophy of History 17 (1): 130–153.

Dierig, Sven. 2006. Wissenschaft in der Maschinenstadt. Emil Du Bois-Reymond und seine Laboratorien in Berlin . Berlin: Wallstein Verlag.

Engber, Daniel. 2013. The Mouse Trap. How one Rodent Rules the Lab. Slate. http://www.slate.com/articles/health_and_science/the_mouse_trap/2011/11/the_mouse_trap.html .

Foucault, Michel. 1975. The Birth of the Clinic: An Archaeology of Medical Perception . New York: Vintage Books.

———. 1979. Discipline and Punish: The Birth of the Prison . New York: Vintage.

Frängsmyr, Tore, J.L. Heilbron, and Robin E. Rider. 1990. The Quantifying Spirit in the Eighteenth Century . Berkeley, CA: University of California Press.

Franklin, Seb. 2015. Control: Digitality as Cultural Logic . Cambridge, MA: MIT Press.

Galen. 1944. Galen on Medical Experience . First edition of the Arabic ed. New York [etc.]: Pub. for the trustees of the late Sir Henry Wellcome by the Oxford University Press.

Galison, Peter. 1987. How Experiments End . Chicago, IL: University of Chicago Press.

———. 2010. Secrecy in Three Acts. Social Research: An International Quarterly 77: 941–974.

Geison, Gerald. 1995. The Private Science of Louis Pasteur . Princeton, NJ: Princeton University Press.

Gigerenzer, Gerd, Zeno G. Swijtink, Theodore M. Porter, Lorraine Daston, John Beatty, and Lorenz Krüger. 1989. The Empire of Chance: How Probability Changed Science and Everyday Life . Cambridge: Cambridge University Press.

Guala, Francesco. 2005. The Methodology of Experimental Economics . Cambridge/New York: Cambridge University Press.

Guettinger, Stephan. 2019. A New Account of Replication in the Experimental Life Sciences. Philosophy of Science 86: 453–471.

Hacking, Ian. 1984. The Emergence of Probability: A Philosophical Study of Early Ideas about Probability, Induction and Statistical Inference . Cambridge: Cambridge University Press.

———. 1985. Do We See Through a Microscope? In Images of Science , ed. P.M. Churchland and C.A. Hooker, 132–152. Chicago and London.

———. 1990. The Taming of Chance . Cambridge: Cambridge University Press.

Hoffmann, Christoph. 2001. The Design of Disturbance: Physics Institutes and Physics Research in Germany, 1870–1910. Perspectives on Science 9: 173–195.

Holbrook, Daniel. 2009. Controlling Contamination: the Origins of Clean Room Technology. History and Technology 25 (3): 173–191. https://doi.org/10.1080/07341510903083203 .

Holman, Bennett. 2020. Humbug, the Council of Pharmacy and Chemistry, and the Origin of “The Blind Test” of Therapeutic Efficacy. In Uncertainty in Pharmacology: Epistemology, Methods and Decisions , ed. Barbara Osimani and A. Lacaze, 397–416. Dordrecht: Springer.

Hughes, Thomas Parke. 1983. Networks of Power: Electrification in Western Society, 1880–1930 . Baltimore, MD: Johns Hopkins University Press.

Kaptchuk, Ted. 1998. Intentional Ignorance: A History of Blind Assessment and Placebo Controls in Medicine. Bulletin of the History of Medicine 72: 389–433.

Keating, Peter, and Alberto Cambrosio. 2012. Cancer on Trial . Chicago, IL: University of Chicago Press.

Kuch, Declan, M. Kearnes, and K. Gulson. 2020. The Promise of Precision: Datafication in Medicine, Agriculture and Education. Policy Studies 41 (5): 527–546. https://doi.org/10.1080/01442872.2020.1724384 .

Kupreeva, Inna. 2022. Galen’s Empiricist Background: A Study of the Argument in On Medical Experience. In Galen’s Epistemology: Experience, Reason, and Method in Ancient Medicine , ed. Matyáš Havrda and R.J. Hankinson, 32–78. Cambridge: Cambridge University Press.

Landecker, Hannah. 2016. It Is What It Eats: Chemically Defined Media and the History of Surrounds. Studies in History and Philosophy of Biological and Biomedical Sciences 57: 148–160.

Leigh, Robert Adam. 2013. On Theriac to Piso, Attributed to Galen. A critical edition with translation and commentary . Exeter: Department of Classics, University of Exeter.

Levin, Miriam. 2000a. Contexts of Control. In Cultures of Control , ed. Miriam Levin, 13–39. Amsterdam: Harwood Academic Publishers.

———, ed. 2000b. Cultures of Control . Amsterdam: Harwood Academic Publishers.

Marks, Harry M. 1997. The Progress of Experiment. Science and Therapeutic Reform in the United States, 1900–1990 . Cambridge: Cambridge University Press.

Mayo, Deborah G. 1996. Error and the Growth of Experimental Knowledge . Chicago: University of Chicago Press.

McCartney, Eugene S. 1942. A Control Experiment in Antiquity. The Classical Weekly 36: 5–6.

McVaugh, Michael. 2009. The ‘Experience-Based’ Medicine of the Thirteenth Century. In Evidence and Interpretation in Studies on Early Science and Medicine , ed. Edith Sylla and William R. Newman, 105–130. Leiden: Brill.

Nasser, M., A. Tibi, and E. Savage-Smith. 2009. Ibn Sīnā’s Canon of Medicine: 11th Century Rules for Assessing the Effects of Drugs. Journal of the Royal Society of Medicine 102: 78–80.

Needham, John Turbervill. 1748. A Summary of Some Late Observations upon the Generation, Composition, and Decomposition of Animal and Vegetable Substances; Communicated in a Letter to Martin Folkes, Esq.; President of the Royal Society, by Mr. Tubervill Needham, Fellow of the Same Society. Philosophical Transactions of the Royal Society of London 45: 615–666.

Parke, Emily C. 2014. Flies from Meat and Wasps from Trees: Reevaluating Francesco Redi’s Spontaneous Generation Experiments. Studies in History and Philosophy of Biological and Biomedical Sciences 45: 34–42.

Article   ADS   PubMed   Google Scholar  

Pastorino, Cesare. 2022. Francis Bacon’s Controlled Experiments on Seed Steeping and Germination: Their Context, Circulation and Methodological Significance. Paper presented at the conference Control Practices in Historical and Systematic Perspectives , Indiana University Bloomington, March 2022.

Paul, Diane B. 1995. Controlling Human Heredity . Atlantic Highlands, NJ: Humanities Press International.

Pauly, Philip J. 1987. Controlling Life. Jacques Loeb & the Engineering Ideal in Biology . Oxford: Oxford University Press.

Popper, Karl. 2002. The Logic of Scientific Discovery . London and New York: Routledge.

Porter, Theodore M. 1995. Trust in Numbers. The Pursuit of Objectivity in Science and Public Life . Princeton, NJ: Princeton University Press.

Rabinbach, Anson. 1990. The Human Motor: Energy, Fatigue, and the Origins of Modernity . Berkeley, CA: University of California Press.

Rader, Karen. 2004. Making Mice: Standardizing Animals for American Biomedical Research . Princeton, NJ: Princeton University Press.

Redi, Francesco. 1909. Experiments on the Generation of Insects , 1688. Chicago, IL: Open Court.

Schickore, Jutta. 2017. About Method: Experimenters, Snake Venom, and the History of Writing Scientifically . Chicago, IL/London: University of Chicago Press.

———. 2021. Methodological Ideas in Past Experimental Inquiry: Rigor Checks Around 1800. Intellectual History Review . https://doi.org/10.1080/17496977.2021.1974225 .

———. 2023. Peculiar Blue Spots: Evidence and Causes around 1800. In Evidence: The Use and Misuse of Data , ed. The American Philosophical Society, 31–55. Philadelphia, PA: American Philosophical Society.

Schlager, Neil. 1995. Breakdown: Deadly Technological Disasters . Dretroit, MI: Visible Ink Press.

Seppel, Marten, and Keith Tribe. 2017. Cameralism in Practice: State Administration and Economy in Early Modern Europe . Rochester, NY: Boydell & Brewer.

Shapin, Steven, and Simon Schaffer. 1985. Leviathan and the Air-Pump. Hobbes, Boyle, and the Experimental Life . Princeton, NJ: Princeton University Press.

Strick, James. 2000. Sparks of Life. Darwinism and the Victorian Debates over Spontaneous Generation . Cambridge, MA: Harvard University Press.

Strong, I.I.I., and C. Frederick. 1999. The History of the Double Blind Test and the Placebo. Journal of Pharmacy and Pharmacology 51: 237–238.

Sullivan, Jacqueline A. 2022. Novel Tool Development and the Dynamics of Control: The Rodent Touchscreen Operant Chamber as a Case Study. Philosophy of Science 89 (5): 1–19.

Article   MathSciNet   Google Scholar  

Treece, James W. 1990. Daniel and the Classic Experimental Design. Accessed January 29, 2023. https://www.icr.org/article/daniel-classic-experimental-design .

Tuchman, Arleen M. 1993. Science, Medicine, and the State in Germany. The Case of Baden, 1815-1871 . New York: Oxford University Press.

Wiener, Norbert. 1948. Cybernetics, or, Control and communication in the animal and the machine . New York/Paris: J. Wiley, Hermann et Cie.

Wimsatt, William C. 2007. Re-Engineering Philosophy for Limited Beings . Cambridge, MA: Harvard University Press.

Wise, M. Norton, ed. 1995. The Values of Precision . Princeton, NJ: Princeton University Press.

Woodward, James. 2008. Cause and Explanation in Psychiatry: An Interventionist Perspective. In Philosophical Issues in Psychiatry: Explanation, Phenomenology and Nosology , ed. K. Kendler and J. Parnas, 132–184. Baltimore, MD: Johns Hopkins University Press.

Worrall, John. 2007. Why There’s No Cause to Randomize. British Journal for the Philosophy of Science 58: 451–488.

Zhmud, Leonid. 2012. Pythagoras and the Early Pythagoreans . Oxford: Oxford University Press.

Download references

Author information

Authors and affiliations.

Department of History and Philosophy of Science and Medicine, Indiana University, Bloomington, IN, USA

Jutta Schickore

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Jutta Schickore .

Editor information

Editors and affiliations.

William R. Newman

Rights and permissions

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Reprints and permissions

Copyright information

© 2024 The Author(s)

About this chapter

Schickore, J. (2024). Introduction: Practices, Strategies, and Methodologies of Experimental Control in Historical Perspective. In: Schickore, J., Newman, W.R. (eds) Elusive Phenomena, Unwieldy Things. Archimedes, vol 71. Springer, Cham. https://doi.org/10.1007/978-3-031-52954-2_1

Download citation

DOI : https://doi.org/10.1007/978-3-031-52954-2_1

Published : 27 February 2024

Publisher Name : Springer, Cham

Print ISBN : 978-3-031-52953-5

Online ISBN : 978-3-031-52954-2

eBook Packages : History History (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• Control Groups and Treatment Groups | Uses & Examples

Control Groups and Treatment Groups | Uses & Examples

Published on July 3, 2020 by Lauren Thomas . Revised on June 22, 2023.

In a scientific study, a control group is used to establish causality by isolating the effect of an independent variable .

Here, researchers change the independent variable in the treatment group and keep it constant in the control group. Then they compare the results of these groups.

Using a control group means that any change in the dependent variable can be attributed to the independent variable. This helps avoid extraneous variables or confounding variables from impacting your work, as well as a few types of research bias , like omitted variable bias .

Table of contents

Control groups in experiments, control groups in non-experimental research, importance of control groups, other interesting articles, frequently asked questions about control groups.

Control groups are essential to experimental design . When researchers are interested in the impact of a new treatment, they randomly divide their study participants into at least two groups:

• The treatment group (also called the experimental group ) receives the treatment whose effect the researcher is interested in.
• The control group receives either no treatment, a standard treatment whose effect is already known, or a placebo (a fake treatment to control for placebo effect ).

The treatment is any independent variable manipulated by the experimenters, and its exact form depends on the type of research being performed. In a medical trial, it might be a new drug or therapy. In public policy studies, it could be a new social policy that some receive and not others.

In a well-designed experiment, all variables apart from the treatment should be kept constant between the two groups. This means researchers can correctly measure the entire effect of the treatment without interference from confounding variables .

• You pay the students in the treatment group for achieving high grades.
• Students in the control group do not receive any money.

Studies can also include more than one treatment or control group. Researchers might want to examine the impact of multiple treatments at once, or compare a new treatment to several alternatives currently available.

• The treatment group gets the new pill.
• Control group 1 gets an identical-looking sugar pill (a placebo)
• Control group 2 gets a pill already approved to treat high blood pressure

Since the only variable that differs between the three groups is the type of pill, any differences in average blood pressure between the three groups can be credited to the type of pill they received.

• The difference between the treatment group and control group 1 demonstrates the effectiveness of the pill as compared to no treatment.
• The difference between the treatment group and control group 2 shows whether the new pill improves on treatments already available on the market.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

Although control groups are more common in experimental research, they can be used in other types of research too. Researchers generally rely on non-experimental control groups in two cases: quasi-experimental or matching design.

Control groups in quasi-experimental design

While true experiments rely on random assignment to the treatment or control groups, quasi-experimental design uses some criterion other than randomization to assign people.

Often, these assignments are not controlled by researchers, but are pre-existing groups that have received different treatments. For example, researchers could study the effects of a new teaching method that was applied in some classes in a school but not others, or study the impact of a new policy that is implemented in one state but not in the neighboring state.

In these cases, the classes that did not use the new teaching method, or the state that did not implement the new policy, is the control group.

Control groups in matching design

In correlational research , matching represents a potential alternate option when you cannot use either true or quasi-experimental designs.

In matching designs, the researcher matches individuals who received the “treatment”, or independent variable under study, to others who did not–the control group.

Each member of the treatment group thus has a counterpart in the control group identical in every way possible outside of the treatment. This ensures that the treatment is the only source of potential differences in outcomes between the two groups.

Control groups help ensure the internal validity of your research. You might see a difference over time in your dependent variable in your treatment group. However, without a control group, it is difficult to know whether the change has arisen from the treatment. It is possible that the change is due to some other variables.

If you use a control group that is identical in every other way to the treatment group, you know that the treatment–the only difference between the two groups–must be what has caused the change.

For example, people often recover from illnesses or injuries over time regardless of whether they’ve received effective treatment or not. Thus, without a control group, it’s difficult to determine whether improvements in medical conditions come from a treatment or just the natural progression of time.

Risks from invalid control groups

If your control group differs from the treatment group in ways that you haven’t accounted for, your results may reflect the interference of confounding variables instead of your independent variable.

Minimizing this risk

A few methods can aid you in minimizing the risk from invalid control groups.

• Ensure that all potential confounding variables are accounted for , preferably through an experimental design if possible, since it is difficult to control for all the possible confounders outside of an experimental environment.
• Use double-blinding . This will prevent the members of each group from modifying their behavior based on whether they were placed in the treatment or control group, which could then lead to biased outcomes.
• Randomly assign your subjects into control and treatment groups. This method will allow you to not only minimize the differences between the two groups on confounding variables that you can directly observe, but also those you cannot.

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

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

Prevent plagiarism. Run a free check.

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

A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

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

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

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

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

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

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

When designing the experiment, you decide:

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

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

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Thomas, L. (2023, June 22). Control Groups and Treatment Groups | Uses & Examples. Scribbr. Retrieved September 9, 2024, from https://www.scribbr.com/methodology/control-group/

Is this article helpful?

Lauren Thomas

Other students also liked, what is a controlled experiment | definitions & examples, random assignment in experiments | introduction & examples, single, double, & triple blind study | definition & examples, "i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

Positive Control vs Negative Control: Differences & Examples

Chris Drew (PhD)

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

Learn about our Editorial Process

A positive control is designed to confirm a known response in an experimental design , while a negative control ensures there’s no effect, serving as a baseline for comparison.

The two terms are defined as below:

• Positive control refers to a group in an experiment that receives a procedure or treatment known to produce a positive result. It serves the purpose of affirming the experiment’s capability to produce a positive outcome.
• Negative control refers to a group that does not receive the procedure or treatment and is expected not to yield a positive result. Its role is to ensure that a positive result in the experiment is due to the treatment or procedure.

The experimental group is then compared to these control groups, which can help demonstrate efficacy of the experimental treatment in comparison to the positive and negative controls.

Positive Control vs Negative Control: Key Terms

Control groups.

A control group serves as a benchmark in an experiment. Typically, it is a subset of participants, subjects, or samples that do not receive the experimental treatment (as in negative control).

This could mean assigning a placebo to a human subject or leaving a sample unaltered in chemical experiments. By comparing the results obtained from the experimental group to the control, you can ascertain whether any differences are due to the treatment or random variability.

A well-configured experimental control is critical for drawing valid conclusions from an experiment. Correct use of control groups permits specificity of findings, ensuring the integrity of experimental data.

See More: Control Variables Examples

The Negative Control

Negative control is a group or condition in an experiment that ought to show no effect from the treatment.

It is useful in ensuring that the outcome isn’t accidental or influenced by an external cause. Imagine a medical test, for instance. You use distilled water, anticipating no reaction, as a negative control.

If a significant result occurs, it warns you of a possible contamination or malfunction during the testing. Failure of negative controls to stay ‘negative’ risks misinterpretation of the experiment’s result, and could undermine the validity of the findings.

The Positive Control

A positive control, on the other hand, affirms an experiment’s functionality by demonstrating a known reaction.

This might be a group or condition where the expected output is known to occur, which you include to ensure that the experiment can produce positive results when they are present. For instance, in testing an antibiotic, a well-known pathogen, susceptible to the medicine, could be the positive control.

Positive controls affirm that under appropriate conditions your experiment can produce a result. Without this reference, experiments could fail to detect true positive results, leading to false negatives. These two controls, used judiciously, are backbones of effective experimental practice.

Experimental Groups

Experimental groups are primarily characterized by their exposure to the examined variable.

That is, these are the test subjects that receive the treatment or intervention under investigation. The performance of the experimental group is then compared against the well-established markers – our positive and negative controls.

For example, an experimental group may consist of rats undergoing a pharmaceutical testing regime, or students learning under a new educational method. Fundamentally, this unit bears the brunt of the investigation and their response powers the outcomes.

However, without positive and negative controls, gauging the results of the experimental group could become erratic. Both control groups exist to highlight what outcomes are expected with and without the application of the variable in question. By comparing results, a clearer connection between the experiment variables and the observed changes surfaces, creating robust and indicative scientific conclusions.

Positive and Negative Control Examples

1. a comparative study of old and new pesticides’ effectiveness.

This hypothetical study aims to evaluate the effectiveness of a new pesticide by comparing its pest-killing potential with old pesticides and an untreated set. The investigation involves three groups: an untouched space (negative control), another treated with an established pesticide believed to kill pests (positive control), and a third area sprayed with the new pesticide (experimental group).

• Negative Control: This group consists of a plot of land infested by pests and not subjected to any pesticide treatment. It acts as the negative control. You expect no decline in pest populations in this area. Any unexpected decrease could signal external influences (i.e. confounding variables ) on the pests unrelated to pesticides, affecting the experiment’s validity.
• Positive Control: Another similar plot, this time treated with a well-established pesticide known to reduce pest populations, constitutes the positive control. A significant reduction in pests in this area would affirm that the experimental conditions are conducive to detect pest-killing effects when a pesticide is applied.
• Experimental Group: This group consists of the third plot impregnated with the new pesticide. Carefully monitoring the pest level in this research area against the backdrop of the control groups will reveal whether the new pesticide is effective or not. Through comparison with the other groups, any difference observed can be attributed to the new pesticide.

2. Evaluating the Effectiveness of a Newly Developed Weight Loss Pill

In this hypothetical study, the effectiveness of a newly formulated weight loss pill is scrutinized. The study involves three groups: a negative control group given a placebo with no weight-reducing effect, a positive control group provided with an approved weight loss pill known to cause a decrease in weight, and an experimental group given the newly developed pill.

• Negative Control: The negative control is comprised of participants who receive a placebo with no known weight loss effect. A significant reduction in weight in this group would indicate confounding factors such as dietary changes or increased physical activity, which may invalidate the study’s results.
• Positive Control: Participants in the positive control group receive an FDA-approved weight loss pill, anticipated to induce weight loss. The success of this control would prove that the experiment conditions are apt to detect the effects of weight loss pills.
• Experimental Group: This group contains individuals receiving the newly developed weight loss pill. Comparing the weight change in this group against both the positive and negative control, any difference observed would offer evidence about the effectiveness of the new pill.

3. Testing the Efficiency of a New Solar Panel Design

This hypothetical study focuses on assessing the efficiency of a new solar panel design. The study involves three sets of panels: a set that is shaded to yield no solar energy (negative control), a set with traditional solar panels that are known to produce an expected level of solar energy (positive control), and a set fitted with the new solar panel design (experimental group).

• Negative Control: The negative control involves a set of solar panels that are deliberately shaded, thus expecting no solar energy output. Any unexpected energy output from this group could point towards measurement errors, needed to be rectified for a valid experiment.
• Positive Control: The positive control set up involves traditional solar panels known to produce a specific amount of energy. If these panels produce the expected energy, it validates that the experiment conditions are capable of measuring solar energy effectively.
• Experimental Group: The experimental group features the new solar panel design. By comparing the energy output from this group against both the controls, any significant output variation would indicate the efficiency of the new design.

4. Investigating the Efficacy of a New Fertilizer on Plant Growth

This hypothetical study investigates the efficacy of a newly formulated fertilizer on plant growth. The study involves three sets of plants: a set without any fertilizer (negative control), a set treated with an established fertilizer known to promote plant growth (positive control), and a third set fed with the new fertilizer (experimental group).

• Negative Control: The negative control involves a set of plants not receiving any fertilizer. Lack of significant growth in this group will confirm that any observed growth in other groups is due to the applied fertilizer rather than other uncontrolled factors.
• Positive Control: The positive control involves another set of plants treated with a well-known fertilizer, expected to promote plant growth. Adequate growth in these plants will validate that the experimental conditions are suitable to detect the influence of a good fertilizer on plant growth.
• Experimental Group: The experimental group consists of the plants subjected to the newly formulated fertilizer. Investigating the growth in this group against the growth in the control groups will provide ascertained evidence whether the new fertilizer is efficient or not.

5. Evaluating the Impact of a New Teaching Method on Student Performance

This hypothetical study aims to evaluate the impact of a new teaching method on students’ performance. This study involves three groups, a group of students taught through traditional methods (negative control), another group taught through an established effective teaching strategy (positive control), and one more group of students taught through the new teaching method (experimental group).

• Negative Control: The negative control comprises students taught by standard teaching methods, where you expect satisfactory but not top-performing results. Any unexpected high results in this group could signal external factors such as private tutoring or independent study, which in turn may distort the experimental outcome.
• Positive Control: The positive control consists of students taught by a known efficient teaching strategy. High performance in this group would prove that the experimental conditions are competent to detect the efficiency of a teaching method.
• Experimental Group: This group consists of students receiving instruction via the new teaching method. By analyzing their performance against both control groups, any difference in results could be attributed to the new teaching method, determining its efficacy.

Table Summary

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 10 Reasons you’re Perpetually Single
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 20 Montessori Toddler Bedrooms (Design Inspiration)
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 21 Montessori Homeschool Setups
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 101 Hidden Talents Examples

Leave a Comment Cancel Reply

Your email address will not be published. Required fields are marked *

Sciencing_Icons_Science SCIENCE

Sciencing_icons_biology biology, sciencing_icons_cells cells, sciencing_icons_molecular molecular, sciencing_icons_microorganisms microorganisms, sciencing_icons_genetics genetics, sciencing_icons_human body human body, sciencing_icons_ecology ecology, sciencing_icons_chemistry chemistry, sciencing_icons_atomic & molecular structure atomic & molecular structure, sciencing_icons_bonds bonds, sciencing_icons_reactions reactions, sciencing_icons_stoichiometry stoichiometry, sciencing_icons_solutions solutions, sciencing_icons_acids & bases acids & bases, sciencing_icons_thermodynamics thermodynamics, sciencing_icons_organic chemistry organic chemistry, sciencing_icons_physics physics, sciencing_icons_fundamentals-physics fundamentals, sciencing_icons_electronics electronics, sciencing_icons_waves waves, sciencing_icons_energy energy, sciencing_icons_fluid fluid, sciencing_icons_astronomy astronomy, sciencing_icons_geology geology, sciencing_icons_fundamentals-geology fundamentals, sciencing_icons_minerals & rocks minerals & rocks, sciencing_icons_earth scructure earth structure, sciencing_icons_fossils fossils, sciencing_icons_natural disasters natural disasters, sciencing_icons_nature nature, sciencing_icons_ecosystems ecosystems, sciencing_icons_environment environment, sciencing_icons_insects insects, sciencing_icons_plants & mushrooms plants & mushrooms, sciencing_icons_animals animals, sciencing_icons_math math, sciencing_icons_arithmetic arithmetic, sciencing_icons_addition & subtraction addition & subtraction, sciencing_icons_multiplication & division multiplication & division, sciencing_icons_decimals decimals, sciencing_icons_fractions fractions, sciencing_icons_conversions conversions, sciencing_icons_algebra algebra, sciencing_icons_working with units working with units, sciencing_icons_equations & expressions equations & expressions, sciencing_icons_ratios & proportions ratios & proportions, sciencing_icons_inequalities inequalities, sciencing_icons_exponents & logarithms exponents & logarithms, sciencing_icons_factorization factorization, sciencing_icons_functions functions, sciencing_icons_linear equations linear equations, sciencing_icons_graphs graphs, sciencing_icons_quadratics quadratics, sciencing_icons_polynomials polynomials, sciencing_icons_geometry geometry, sciencing_icons_fundamentals-geometry fundamentals, sciencing_icons_cartesian cartesian, sciencing_icons_circles circles, sciencing_icons_solids solids, sciencing_icons_trigonometry trigonometry, sciencing_icons_probability-statistics probability & statistics, sciencing_icons_mean-median-mode mean/median/mode, sciencing_icons_independent-dependent variables independent/dependent variables, sciencing_icons_deviation deviation, sciencing_icons_correlation correlation, sciencing_icons_sampling sampling, sciencing_icons_distributions distributions, sciencing_icons_probability probability, sciencing_icons_calculus calculus, sciencing_icons_differentiation-integration differentiation/integration, sciencing_icons_application application, sciencing_icons_projects projects, sciencing_icons_news news.

• Share Tweet Email Print
• Home ⋅
• Science ⋅
• Physics ⋅
• Fundamentals

What Are Dependent, Independent & Controlled Variables?

What Is a Responding Variable in Science Projects?

Say you're in lab, and your teacher asks you to design an experiment. The experiment must test how plants grow in response to different colored light. How would you begin? What are you changing? What are you keeping the same? What are you measuring?

These parameters of what you would change and what you would keep the same are called variables. Take a look at how all of these parameters in an experiment are defined, as independent, dependent and controlled variables.

What Is a Variable?

A variable is any quantity that you are able to measure in some way. This could be temperature, height, age, etc. Basically, a variable is anything that contributes to the outcome or result of your experiment in any way.

In an experiment there are multiple kinds of variables: independent, dependent and controlled variables.

What Is an Independent Variable?

An independent variable is the variable the experimenter controls. Basically, it is the component you choose to change in an experiment. This variable is not dependent on any other variables.

For example, in the plant growth experiment, the independent variable is the light color. The light color is not affected by anything. You will choose different light colors like green, red, yellow, etc. You are not measuring the light.

What Is a Dependent Variable?

A dependent variable is the measurement that changes in response to what you changed in the experiment. This variable is dependent on other variables; hence the name! For example, in the plant growth experiment, the dependent variable would be plant growth.

You could measure this by measuring how much the plant grows every two days. You could also measure it by measuring the rate of photosynthesis. Either of these measurements are dependent upon the kind of light you give the plant.

What Are Controlled Variables?

A control variable in science is any other parameter affecting your experiment that you try to keep the same across all conditions.

For example, one control variable in the plant growth experiment could be temperature. You would not want to have one plant growing in green light with a temperature of 20°C while another plant grows in red light with a temperature of 27°C.

You want to measure only the effect of light, not temperature. For this reason you would want to keep the temperature the same across all of your plants. In other words, you would want to control the temperature.

Another example is the amount of water you give the plant. If one plant receives twice the amount of water as another plant, there would be no way for you to know that the reason those plants grew the way they did is due only to the light color their received.

The observed effect could also be due in part to the amount of water they got. A control variable in science experiments is what allows you to compare other things that may be contributing to a result because you have kept other important things the same across all of your subjects.

Graphing Your Experiment

When graphing the results of your experiment, it is important to remember which variable goes on which axis.

The independent variable is graphed on the x-axis . The dependent variable , which changes in response to the independent variable, is graphed on the y-axis . Controlled variables are usually not graphed because they should not change. They could, however, be graphed as a verification that other conditions are not changing.

For example, after graphing the growth as compared to light, you could also look at how the temperature varied across different conditions. If you notice that it did vary quite a bit, you may need to go back and look at your experimental setup: How could you improve the experiment so that all plants are exposed to as similar an environment as possible (aside from the light color)?

How to Remember Which is Which

In order to try and remember which is the dependent variable and which is the independent variable, try putting them into a sentence which uses "causes a change in."

Here's an example. Saying, "light color causes a change in plant growth," is possible. This shows us that the independent variable affects the dependent variable. The inverse, however, is not true. "Plant growth causes a change in light color," is not possible. This way you know which is the independent variable and which is the dependent variable!

Related Articles

What are constants & controls of a science project..., what are independent & dependent variables in science..., difference between manipulative & responding variable, how to collect data from a science project, science fair projects on plants: do they grow faster..., how to write a testable hypothesis, how to grow a plant from a bean as a science project, what is the role of carotenoids in photosynthesis, proper way to label a graph, two week science projects, why should you only test for one variable at a time..., definitions of control, constant, independent and dependent..., what is a constant in a science fair project, science projects on which fertilizer makes a plant..., venus flytrap science projects, the effect of temperature on the rate of photosynthesis, phototropism experiments, cool science project ideas for k-4th grade, measuring wet bulb temperature.

• NCES Kids: What are Independent and Dependent Variables?
• Khan Academy: Dependent and independent variables review (article)

About the Author

Riti Gupta holds a Honors Bachelors degree in Biochemistry from the University of Oregon and a PhD in biology from Johns Hopkins University. She has an interest in astrobiology and manned spaceflight. She has over 10 years of biology research experience in academia. She currently teaches classes in biochemistry, biology, biophysics, astrobiology, as well as high school AP Biology and Chemistry test prep.

Find Your Next Great Science Fair Project! GO