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What An Experimental Control Is And Why It’s So Important

Daniel Nelson

An experimental control is used in scientific experiments to minimize the effect of variables which are not the interest of the study. The control can be an object, population, or any other variable which a scientist would like to “control.”

You may have heard of experimental control, but what is it? Why is an experimental control important? The function of an experimental control is to hold constant the variables that an experimenter isn’t interested in measuring.

This helps scientists ensure that there have been no deviations in the environment of the experiment that could end up influencing the outcome of the experiment, besides the variable they are investigating. Let’s take a closer look at what this means.

You may have ended up here to understand why a control is important in an experiment. A control is important for an experiment because it allows the experiment to minimize the changes in all other variables except the one being tested.

To start with, it is important to define some terminology.

Terminology Of A Scientific Experiment

Randomization is important as it allows for more non-biased results in experiments. Random numbers generators are often used both in scientific studies as well as on 지노 사이트 to make outcomes fairer.

Scientists use the scientific method to ask questions and come to conclusions about the nature of the world. After making an observation about some sort of phenomena they would like to investigate, a scientist asks what the cause of that phenomena could be. The scientist creates a hypothesis, a proposed explanation that answers the question they asked. A hypothesis doesn’t need to be correct, it just has to be testable.

The hypothesis is a prediction about what will happen during the experiment, and if the hypothesis is correct then the results of the experiment should align with the scientist’s prediction. If the results of the experiment do not align with the hypothesis, then a good scientist will take this data into consideration and form a new hypothesis that can better explain the phenomenon in question.

Independent and Dependent Variables

In order to form an effective hypothesis and do meaningful research, the researcher must define the experiment’s independent and dependent variables . The independent variable is the variable which the experimenter either manipulates or controls in an experiment to test the effects of this manipulation on the dependent variable. A dependent variable is a variable being measured to see if the manipulation has any effect.

Photo: frolicsomepl via Pixabay, CC0

For instance, if a researcher wanted to see how temperature impacts the behavior of a certain gas, the temperature they adjust would be the independent variable and the behavior of the gas the dependent variable.

Control Groups and Experimental Groups

There will frequently be two groups under observation in an experiment, the experimental group, and the control group . The control group is used to establish a baseline that the behavior of the experimental group can be compared to. If two groups of people were receiving an experimental treatment for a medical condition, one would be given the actual treatment (the experimental group) and one would typically be given a placebo or sugar pill (the control group).

Without an experimental control group, it is difficult to determine the effects of the independent variable on the dependent variable in an experiment. This is because there can always be outside factors that are influencing the behavior of the experimental group. The function of a control group is to act as a point of comparison, by attempting to ensure that the variable under examination (the impact of the medicine) is the thing responsible for creating the results of an experiment. The control group is holding other possible variables constant, such as the act of seeing a doctor and taking a pill, so only the medicine itself is being tested.

Why Are Experimental Controls So Important?

Experimental controls allow scientists to eliminate varying amounts of uncertainty in their experiments. Whenever a researcher does an experiment and wants to ensure that only the variable they are interested in changing is changing, they need to utilize experimental controls.

Experimental controls have been dubbed “controls” precisely because they allow researchers to control the variables they think might have an impact on the results of the study. If a researcher believes that some outside variables could influence the results of their research, they’ll use a control group to try and hold that thing constant and measure any possible influence it has on the results. It is important to note that there may be many different controls for an experiment, and the more complex a phenomenon under investigation is, the more controls it is likely to have.

Not only do controls establish a baseline that the results of an experiment can be compared to, they also allow researchers to correct for possible errors. If something goes wrong in the experiment, a scientist can check on the controls of the experiment to see if the error had to do with the controls. If so, they can correct this next time the experiment is done.

A Practical Example

Let’s take a look at a concrete example of experimental control. If an experimenter wanted to determine how different soil types impacted the germination period of seeds , they could set up four different pots. Each pot would be filled with a different soil type, planted with seeds, then watered and exposed to sunlight. Measurements would be taken regarding how long it took for the seeds to sprout in the different soil types.

Photo: Kaz via Pixabay, CC0

A control for this experiment might be to fill more pots with just the different types of soil and no seeds or to set aside some seeds in a pot with no soil. The goal is to try and determine that it isn’t something else other than the soil, like the nature of the seeds themselves, the amount of sun they were exposed to, or how much water they are given, that affected how quickly the seeds sprouted. The more variables a researcher controlled for, the surer they could be that it was the type of soil having an impact on the germination period.

  Not All Experiments Are Controlled

“It doesn’t matter how beautiful your theory is, it doesn’t matter how smart you are. If it doesn’t agree with experiment, it’s wrong.” — Richard P. Feynman

While experimental controls are important , it is also important to remember that not all experiments are controlled. In the real world, there are going to be limitations on what variables a researcher can control for, and scientists often try to record as much data as they can during an experiment so they can compare factors and variables with one another to see if any variables they didn’t control for might have influenced the outcome. It’s still possible to draw useful data from experiments that don’t have controls, but it is much more difficult to draw meaningful conclusions based on uncontrolled data.

Though it is often impossible in the real world to control for every possible variable, experimental controls are an invaluable part of the scientific process and the more controls an experiment has the better off it is.

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• 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

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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

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What Is the Purpose of a Control in an Experiment?

Damon verial, 26 sep 2017.

An experiment without a control is not an experiment; that’s how essential the control is. When a scientist speaks of a control, however, she might mean one of two things: a group of subjects not submitted to a treatment or the management of a nuisance factor in an experiment. Either way, without a control, a scientist cannot make a conclusion about the relationship between independent and dependent variables.

Explore this article

• “Control” Defines “Experiment”
• Talking to Plants Leads to Growth
• Doing Nothing Equals Control
• “All Things Being Equal”

1 “Control” Defines “Experiment”

Though a natural inquiry of science students and even graduate students who should know better, according to the University of Colorado, the question “What is the purpose of a control in an experiment?” is equivalent to the question “What is the purpose of vegetables in a salad?” With the exception of fruit salads and pasta salads, the answer to both of these questions is that you can’t have the latter without the former. A study with a control has the basic requirement of being an experiment. A study without a control is a non-experimental study.

2 Talking to Plants Leads to Growth

Remember that an experiment contains two variables of interest: the independent variable and the dependent variable. A scientist running an experiment will vary the independent variable and observe changes in the dependent variable. But according to Roger Kirk, distinguished professor of statistics and author of “Experimental Design,” such an experiment is not complete without considering other factors that might have roles in affecting the dependent variable. For example, an experimenter who suspects that talking to plants helps them grow might assign to his experiment the dependent variable “growth” and the independent variable “amount of words spoken to plant.” He could then talk to the plants and witness growth, attributing his words spoken to the growth of the plants. But it’s what he didn’t consider that might be the main reason for growth: aspects such as sunlight, temperature, and water given. His experiment lacked control.

3 Doing Nothing Equals Control

When scientists refer to a “control,” they could be referring to one of two things. The first is the control group. The control group is a group of subjects in an experiment that lack an independent variable or have a “standard” value for the experiment’s independent variable, such as zero. A control group allows a scientist to compare it to the other group or groups in an experiment. If a scientist notices a significant difference between the control group and one or more of the other groups, he can logically lead to the conclusion that the independent variable has an impact on the dependent variable. For example, a scientist talking to plants will need another group of plants, the control, to undergo the same experiment with the independent variable, “amount of words spoken to plant,” being equal to zero. Then, all things being equal, if he witnesses the “listening” plants growing more quickly than the “deaf” plants, he can justify his hypothesis that plants grow faster when spoken to. In a sense, when the scientist doubles his subjects and does nothing special to half, he has a control.

4 “All Things Being Equal”

A scientist using a control group must ensure that the control group is equal to the experimental group, or treatment group, in every way except the independent variable. This is the other meaning of “control.” A scientist working with plants might control the amount of sunlight all the plants get. He would also control their water intake, temperature and placement in a room. When he does this to both the control group and the treatment group, he can be certain that it is only the variation in the independent variable that leads to a variation in the dependent variable between the control group and treatment group. Without controlling these “nuisance factors,” a scientist cannot conclude the existence of a relationship between the dependent variable and the independent variable, which is often the main goal of an experiment.

• 1 University of Colorado: Revealing Student Thinking about Experimental Design and the Roles of Control Experiments
• 2 Corwin: Experimental Design

About the Author

Having obtained a Master of Science in psychology in East Asia, Damon Verial has been applying his knowledge to related topics since 2010. Having written professionally since 2001, he has been featured in financial publications such as SafeHaven and the McMillian Portfolio. He also runs a financial newsletter at Stock Barometer.

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Understanding Experimental Controls

• Experimentation

Much of the training that scientists receive in graduate school is experiential, you learn how to do an experiment by working in a laboratory and performing experiments. In my opinion, not enough time and effort is devoted to understanding the philosophy and methods of experimental design.

An experiment without the proper controls is meaningless. Controls allow the experimenter to minimize the effects of factors other than the one being tested. It’s how we know an experiment is testing the thing it claims to be testing.

This goes beyond science — controls are necessary for any sort of experimental testing, no matter the subject area. This is often why so many bibliometric studies of the research literature are so problematic. Inadequate controls are often performed which fail to eliminate the effects of confounding factors, leaving the causality of any effect seen to be undetermined.

Novartis’ David Glass has put together the videos below, showing some of the basics of experimental validation and controls (Full disclosure: I was an editor on the first edition of David’s book on experimental design). These short videos offer quick lessons in positive and negative controls, as well as how to validate your experimental system.

These are great starting points, and I highly recommend Glass’ book, now in its second edition , if you want to dig deeper and understand the nuances of the different types of negative and positive controls, not to mention method and reagent controls, subject controls, assumption controls and experimentalist controls.

David Crotty

David Crotty is a Senior Consultant at Clarke & Esposito, a boutique management consulting firm focused on strategic issues related to professional and academic publishing and information services. Previously, David was the Editorial Director, Journals Policy for Oxford University Press. He oversaw journal policy across OUP’s journals program, drove technological innovation, and served as an information officer. David acquired and managed a suite of research society-owned journals with OUP, and before that was the Executive Editor for Cold Spring Harbor Laboratory Press, where he created and edited new science books and journals, along with serving as a journal Editor-in-Chief. He has served on the Board of Directors for the STM Association, the Society for Scholarly Publishing and CHOR, Inc., as well as The AAP-PSP Executive Council. David received his PhD in Genetics from Columbia University and did developmental neuroscience research at Caltech before moving from the bench to publishing.

7 Thoughts on "Understanding Experimental Controls"

We could add one more necessary control in this experiment–controlling for variability in individual response.

In the three videos, the experimenter may only detect differences between groups (or average differences). He is unable to detect changes in individuals. Some participants may be more sensitive to caffeine than others, some may show negative changes, and some may show no changes at all. If we take the blood pressure of participants before they drink coffee, we have a baseline measurement for all individuals. We also have a check on whether the experimenter was able to randomly assign participants to each treatment group.

In effect, each individual is their own control, with a before and after measurement. The experimenter is looking at the change in response of the individual rather than the average effect of the group. It is a much more sensitive way to structure and analyze experiments like this.

• By Phil Davis
• Nov 2, 2018, 8:57 AM

Agreed, these videos only skim the surface (his book goes into much greater detail about a much wider range of controls).

• By David Crotty
• Nov 2, 2018, 9:05 AM

Most experimenters who use random assignment to control and treatment groups have found that post-test only design works as well as pre-/post-test design.

• Nov 2, 2018, 10:01 AM

I don’t see how. By controlling for a potentially large source of variability—the individual participant—statistical tests become much more sensitive to changes than averaging all of that variability by group in a simple post-test design. Second, it is a check to see whether the randomization of participants into groups was successful. In many RTCs in the clinical sciences, there is recruitment bias, allowing for the sicker patients to be placed in the treatment group, for example.

• Nov 2, 2018, 12:55 PM

No mention of Institutional Review Board?! The IRB will raise Dr. Johnson’s own blood pressure.

And then there’s the issue of Dr. Johnson’s White Coat — that might trigger considerable individual variation. (My own blood pressure readings change markedly in the course of a visit to the doctor. )

• Nov 2, 2018, 4:59 PM

I believe that IRB approval is discussed in the video on system validation.

• Nov 2, 2018, 5:02 PM

Late to the debate, but I think those are wonderful. Maybe next Control Kitty will ask just how he assembled all those volunteers for his test to be representative and blinding to minimize bias. Were they self-selected? A bunch of caffeine habituated javaheads who responded to an ad in the coffee shop? I could see another video on randomization and sampling frames. I’m sure David Glass’s book goes into all that, but well, I have a shelf full of related books and I’m unlikely to benefit from and want to buy another. Unless maybe he hooks with another clever video or two. Go Kitty! Except, ~900 views! That’s sad. I might have sneak in citations to them. (I tend to get chastised by reviewers/editors for citing non-scholarly sources.) Something like this might slip under the editor’s radar: Glass, D. 2018. Experimental Design for Biologists: 1. System Validation. Video (4:06 minutes). YouTube. https://www.youtube.com/watch?v=qK9fXYDs–8 [Accessed November 11, 2018].

• By Chris Mebane
• Nov 12, 2018, 12:17 AM

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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.

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Control Group vs Experimental Group

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In a controlled experiment , scientists compare a control group, and an experimental group is identical in all respects except for one difference – experimental manipulation.

Differences

Unlike the experimental group, the control group is not exposed to the independent variable under investigation. So, it 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.

Almost all experimental studies are designed to include a control group and one or more experimental groups. In most cases, participants are randomly assigned to either a control or experimental group.

Because participants are randomly assigned to either group, we can assume that the groups are identical except for manipulating the independent variable in the experimental group.

It is important that every aspect of the experimental environment is the same and that the experimenters carry out the exact same procedures with both groups so researchers can confidently conclude that any differences between groups are actually due to the difference in treatments.

Control Group

A control group consists of participants who do not receive any experimental treatment. The control participants serve as a comparison group.

The control group is matched as closely as possible to the experimental group, including age, gender, social class, ethnicity, etc.

The difference between the control and experimental groups is that the control group is not exposed to the independent variable , which is thought to be the cause of the behavior being investigated.

Researchers will compare the individuals in the control group to those in the experimental group to isolate the independent variable and examine its impact.

The control group is important because it serves as a baseline, enabling researchers to see what impact changes to the independent variable produce and strengthening researchers’ ability to draw conclusions from a study.

Without the presence of a control group, a researcher cannot determine whether a particular treatment truly has an effect on an experimental group.

Control groups are critical to the scientific method as they help ensure the internal validity of a study.

Assume you want to test a new medication for ADHD . One group would receive the new medication, and the other group would receive a pill that looked exactly the same as the one that the others received, but it would be a placebo. The group that takes the placebo would be the control group.

Types of Control Groups

Positive control group.

• A positive control group is an experimental control that will produce a known response or the desired effect.
• A positive control is used to ensure a test’s success and confirm an experiment’s validity.
• For example, when testing for a new medication, an already commercially available medication could serve as the positive control.

Negative Control Group

• A negative control group is an experimental control that does not result in the desired outcome of the experiment.
• A negative control is used to ensure that there is no response to the treatment and help identify the influence of external factors on the test.
• An example of a negative control would be using a placebo when testing for a new medication.

Experimental Group

An experimental group consists of participants exposed to a particular manipulation of the independent variable. These are the participants who receive the treatment of interest.

Researchers will compare the responses of the experimental group to those of a control group to see if the independent variable impacted the participants.

An experiment must have at least one control group and one experimental group; however, a single experiment can include multiple experimental groups, which are all compared against the control group.

Having multiple experimental groups enables researchers to vary different levels of an experimental variable and compare the effects of these changes to the control group and among each other.

Assume you want to study to determine if listening to different types of music can help with focus while studying.

You randomly assign participants to one of three groups: one group that listens to music with lyrics, one group that listens to music without lyrics, and another group that listens to no music.

The group of participants listening to no music while studying is the control group, and the groups listening to music, whether with or without lyrics, are the two experimental groups.

Frequently Asked Questions

1. what is the difference between the control group and the experimental group in an experimental study.

Put simply; an experimental group is a group that receives the variable, or treatment, that the researchers are testing, whereas the control group does not. These two groups should be identical in all other aspects.

2. What is the purpose of a control group in an experiment

A control group is essential in experimental research because it:

Provides a baseline against which the effects of the manipulated variable (the independent variable) can be measured.

Helps to ensure that any changes observed in the experimental group are indeed due to the manipulation of the independent variable and not due to other extraneous or confounding factors.

Helps to account for the placebo effect, where participants’ beliefs about the treatment can influence their behavior or responses.

In essence, it increases the internal validity of the results and the confidence we can have in the conclusions.

3. Do experimental studies always need a control group?

Not all experiments require a control group, but a true “controlled experiment” does require at least one control group. For example, experiments that use a within-subjects design do not have a control group.

In  within-subjects designs , all participants experience every condition and are tested before and after being exposed to treatment.

These experimental designs tend to have weaker internal validity as it is more difficult for a researcher to be confident that the outcome was caused by the experimental treatment and not by a confounding variable.

4. Can a study include more than one control group?

Yes, studies can include multiple control groups. For example, if several distinct groups of subjects do not receive the treatment, these would be the control groups.

5. How is the control group treated differently from the experimental groups?

The control group and the experimental group(s) are treated identically except for one key difference: exposure to the independent variable, which is the factor being tested. The experimental group is subjected to the independent variable, whereas the control group is not.

This distinction allows researchers to measure the effect of the independent variable on the experimental group by comparing it to the control group, which serves as a baseline or standard.

Bailey, R. A. (2008). Design of Comparative Experiments. Cambridge University Press. ISBN 978-0-521-68357-9.

Hinkelmann, Klaus; Kempthorne, Oscar (2008). Design and Analysis of Experiments, Volume I: Introduction to Experimental Design (2nd ed.). Wiley. ISBN 978-0-471-72756-9.

What Is a Control Group?

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A control group in a scientific experiment is a group separated from the rest of the experiment, where the independent variable being tested cannot influence the results. This isolates the independent variable's effects on the experiment and can help rule out alternative explanations of the experimental results.

A control group definition can also be separated into two other types: positive or negative.

Positive control groups are groups where the conditions of the experiment are set to guarantee a positive result. A positive control group can show the experiment is functioning properly as planned.

Negative control groups are groups where the conditions of the experiment are set to cause a negative outcome.

Control groups are not necessary for all scientific experiments. Controls are extremely useful when the experimental conditions are complex and difficult to isolate.

Example of a Negative Control Group

Negative control groups are particularly common in science fair experiments , to teach students how to identify the independent variable . A simple example of a control group can be seen in an experiment in which the researcher tests whether or not a new fertilizer affects plant growth. The negative control group would be the plants grown without fertilizer but under the same conditions as the experimental group. The only difference between the experimental group would be whether or not the fertilizer was used.

Several experimental groups could differ in the fertilizer concentration, application method, etc. The null hypothesis would be that the fertilizer does not affect plant growth. Then, if a difference is seen in the growth rate or the height of plants over time, a strong correlation between fertilizer and growth would be established. Note the fertilizer could have a negative impact on growth rather than positive. Or, for some reason, the plants might not grow at all. The negative control group helps establish the experimental variable is the cause of atypical growth rather than some other (possibly unforeseen) variable.

Example of a Positive Control Group

A positive control demonstrates an experiment is capable of producing a positive result. For example, let's say you are examining bacterial susceptibility to a drug. You might use a positive control to make sure the growth medium is capable of supporting any bacteria. You could culture bacteria known to carry the drug resistance marker, so they should be capable of surviving on a drug-treated medium. If these bacteria grow, you have a positive control that shows other drug-resistant bacteria should be capable of surviving the test.

The experiment could also include a negative control. You could plate bacteria known not to carry a drug-resistant marker. These bacteria should be unable to grow on the drug-laced medium. If they do grow, you know there is a problem with the experiment .

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Chapter 12: Statistics in Practice

Back to chapter, controls in experiments, previous video 12.6: crossover experiments, next video 12.10: clinical trials.

Controls in an experiment are elements that are held constant and not affected by independent variables. Controls are essential for unbiased and accurate measurement of the dependent variables in response to the treatment.

For example, patients reporting in a hospital with high-grade fever, breathing difficulty, cough, cold, and severe body pain are suspected of COVID infection. But it is  also possible that other respiratory infection causes the same symptoms. So, the doctor recommends a COVID test.

The patient's nasal swabs are collected, and the  COVID test is performed. In addition, a control sample is maintained that does not have COVID viral RNA. This type of control is also called negative control. It helps to prevent false positive reports in patients' samples.

A positive control is another commonly used type of control in an experiment. Unlike the negative control, the positive control contains an actual sample – the viral RNA. This helps to match the presence of viral RNA in the test samples, and it validates the procedure and accuracy of the test.

When conducting an experiment, it is crucial to have control to reduce bias and accurately measure the dependent variables. It also marks the results more reliable. Controls are elements in an experiment that have the same characteristics as the treatment groups but are not affected by the independent variable. By sorting these data into control and experimental conditions, the relationship between the dependent and independent variables can be drawn. A randomized experiment always includes a control group that receives an inactive treatment but is otherwise managed exactly as the other groups. The control group helps researchers balance the effects of being in an experiment with the effects of the active treatments.

In clinical or diagnostic procedures, positive controls are included to validate the test results. The positive controls would show the expected result if the test had worked as expected. A negative control does not contain the main ingredient or treatment but includes everything else. For example, in a COVID RT-PCR test, a negative sample does not include the viral DNA. Experiments often use positive and negative controls to prevent or avoid false positives and false negative reports. In

This text is adapted from Openstax, Introductory Statistics, Section 1.4, Experimental Design and Ethics

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Why control an experiment?

John s torday.

1 Department of Pediatrics, Harbor‐UCLA Medical Center, Torrance, CA, USA

František Baluška

2 IZMB, University of Bonn, Bonn, Germany

Empirical research is based on observation and experimentation. Yet, experimental controls are essential for overcoming our sensory limits and generating reliable, unbiased and objective results.

We made a deliberate decision to become scientists and not philosophers, because science offers the opportunity to test ideas using the scientific method. And once we began our formal training as scientists, the greatest challenge beyond formulating a testable or refutable hypothesis was designing appropriate controls for an experiment. In theory, this seems trivial, but in practice, it is often difficult. But where and when did this concept of controlling an experiment start? It is largely attributed to Roger Bacon, who emphasized the use of artificial experiments to provide additional evidence for observations in his Novum Organum Scientiarum in 1620. Other philosophers took up the concept of empirical research: in 1877, Charles Peirce redefined the scientific method in The Fixation of Belief as the most efficient and reliable way to prove a hypothesis. In the 1930s, Karl Popper emphasized the necessity of refuting hypotheses in The Logic of Scientific Discoveries . While these influential works do not explicitly discuss controls as an integral part of experiments, their importance for generating solid and reliable results is nonetheless implicit.

… once we began our formal training as scientists, the greatest challenge beyond formulating a testable or refutable hypothesis was designing appropriate controls for an experiment.

But the scientific method based on experimentation and observation has come under criticism of late in light of the ever more complex problems faced in physics and biology. Chris Anderson, the editor of Wired Magazine, proposed that we should turn to statistical analysis, machine learning, and pattern recognition instead of creating and testing hypotheses, based on the Informatics credo that if you cannot answer the question, you need more data. However, this attitude subsumes that we already have enough data and that we just cannot make sense of it. This assumption is in direct conflict with David Bohm's thesis that there are two “Orders”, the Explicate and Implicate 1 . The Explicate Order is the way in which our subjective sensory systems perceive the world 2 . In contrast, Bohm's Implicate Order would represent the objective reality beyond our perception. This view—that we have only a subjective understanding of reality—dates back to Galileo Galilei who, in 1623, criticized the Aristotelian concept of absolute and objective qualities of our sensory perceptions 3 and to Plato's cave allegory that reality is only what our senses allow us to see.

The only way for systematically overcoming the limits of our sensory apparatus and to get a glimpse of the Implicate Order is through the scientific method, through hypothesis‐testing, controlled experimentation. Beyond the methodology, controlling an experiment is critically important to ensure that the observed results are not just random events; they help scientists to distinguish between the “signal” and the background “noise” that are inherent in natural and living systems. For example, the detection method for the recent discovery of gravitational waves used four‐dimensional reference points to factor out the background noise of the Cosmos. Controls also help to account for errors and variability in the experimental setup and measuring tools: The negative control of an enzyme assay, for instance, tests for any unrelated background signals from the assay or measurement. In short, controls are essential for the unbiased, objective observation and measurement of the dependent variable in response to the experimental setup.

The only way for systematically overcoming the limits of our sensory apparatus […] is through the Scientific Method, through hypothesis‐testing, controlled experimentation.

Nominally, both positive and negative controls are material and procedural; that is, they control for variability of the experimental materials and the procedure itself. But beyond the practical issues to avoid procedural and material artifacts, there is an underlying philosophical question. The need for experimental controls is a subliminal recognition of the relative and subjective nature of the Explicate Order. It requires controls as “reference points” in order to transcend it, and to approximate the Implicate Order.

This is similar to Peter Rowlands’ 4 dictum that everything in the Universe adds up to zero, the universal attractor in mathematics. Prior to the introduction of zero, mathematics lacked an absolute reference point similar to a negative or positive control in an experiment. The same is true of biology, where the cell is the reference point owing to its negative entropy: It appears as an attractor for the energy of its environment. Hence, there is a need for careful controls in biology: The homeostatic balance that is inherent to life varies during the course of an experiment and therefore must be precisely controlled to distinguish noise from signal and approximate the Implicate Order of life.

P  < 0.05 tacitly acknowledges the explicate order

Another example of the “subjectivity” of our perception is the level of accuracy we accept for differences between groups. For example, when we use statistical methods to determine if an observed difference between control and experimental groups is a random occurrence or a specific effect, we conventionally consider a p value of less than or equal to 5% as statistically significant; that is, there is a less than 0.05 probability that the effect is random. The efficacy of this arbitrary convention has been debated for decades; suffice to say that despite questioning the validity of that convention, a P value of < 0.05 reflects our acceptance of the subjectivity of our perception of reality.

… controls are essential for the unbiased, objective observation and measurement of the dependent variable in response to the experimental setup.

Thus, if we do away with hypothesis‐testing science in favor of informatics based on data and statistics—referring to Anderson's suggestion—it reflects our acceptance of the noise in the system. However, mere data analysis without any underlying hypothesis is tantamount to “garbage in‐garbage out”, in contrast to well‐controlled imaginative experiments to separate the wheat from the chaff. Albert Einstein was quoted as saying that imagination was more important than knowledge.

The ultimate purpose of the scientific method is to understand ourselves and our place in Nature. Conventionally, we subscribe to the Anthropic Principle, that we are “in” this Universe, whereas the Endosymbiosis Theory, advocated by Lynn Margulis, stipulates that we are “of” this Universe as a result of the assimilation of the physical environment. According to this theory, the organism endogenizes external factors to make them physiologically “useful”, such as iron as the core of the hemoglobin molecule, or ancient bacteria as mitochondria.

… there is a fundamental difference between knowing via believing and knowing based on empirical research.

By applying the developmental mechanism of cell–cell communication to phylogeny, we have revealed the interrelationships between cells and explained evolution from its origin as the unicellular state to multicellularity via cell–cell communication. The ultimate outcome of this research is that consciousness is the product of cellular processes and cell–cell communication in order to react to the environment and better anticipate future events 5 , 6 . Consciousness is an essential prerequisite for transcending the Explicate Order toward the Implicate Order via cellular sensory and cognitive systems that feed an ever‐expanding organismal knowledge about both the environment and itself.

It is here where the empirical approach to understanding nature comes in with its emphasis that knowledge comes only from sensual experience rather than innate ideas or traditions. In the context of the cell or higher systems, knowledge about the environment can only be gained by sensing and analyzing the environment. Empiricism is similar to an equation in which the variables and terms form a product, or a chemical reaction, or a biological process where the substrates, aka sensory data, form products, that is, knowledge. However, it requires another step—imagination, according to Albert Einstein—to transcend the Explicate Order in order to gain insight into the Implicate Order. Take for instance, Dmitri Ivanovich Mendeleev's Periodic Table of Elements: his brilliant insight was not just to use Atomic Number to organize it, but also to consider the chemical reactivities of the Elements by sorting them into columns. By introducing chemical reactivity to the Periodic Table, Mendeleev provided something like the “fourth wall” in Drama, which gives the audience an omniscient, god‐like perspective on what is happening on stage.

The capacity to transcend the subjective Explicate Order to approximate the objective Implicate Order is not unlike Eastern philosophies like Buddhism or Taoism, which were practiced long before the scientific method. An Indian philosopher once pointed out that the Hindus have known for 30,000 years that the Earth revolves around the sun, while the Europeans only realized this a few hundred years ago based on the work of Copernicus, Brahe, and Galileo. However, there is a fundamental difference between knowing via believing and knowing based on empirical research. A similar example is Aristotle's refusal to test whether a large stone would fall faster than a small one, as he knew the answer already 7 . Galileo eventually performed the experiment from the Leaning Tower in Pisa to demonstrate that the fall time of two objects is independent of their mass—which disproved Aristotle's theory of gravity that stipulated that objects fall at a speed proportional to their mass. Again, it demonstrates the power of empiricism and experimentation as formulated by Francis Bacon, John Locke, and others, over intuition and rationalizing.

Even if our scientific instruments provide us with objective data, we still need to apply our consciousness to evaluate and interpret such data.

Following the evolution from the unicellular state to multicellular organisms—and reverse‐engineering it to a minimal‐cell state—reveals that biologic diversity is an artifact of the Explicate Order. Indeed, the unicell seems to be the primary level of selection in the Implicate Order, as it remains proximate to the First Principles of Physiology, namely negative entropy (negentropy), chemiosmosis, and homeostasis. The first two principles are necessary for growth and proliferation, whereas the last reflects Newton's Third Law of Motion that every action has an equal and opposite reaction so as to maintain homeostasis.

All organisms interact with their surroundings and assimilate their experience as epigenetic marks. Such marks extend to the DNA of germ cells and thus change the phenotypic expression of the offspring. The offspring, in turn, interacts with the environment in response to such epigenetic modifications, giving rise to the concept of the phenotype as an agent that actively and purposefully interacts with its environment in order to adapt and survive. This concept of phenotype based on agency linked to the Explicate Order fundamentally differs from its conventional description as a mere set of biologic characteristics. Organisms’ capacities to anticipate future stress situations from past memories are obvious in simple animals such as nematodes, as well as in plants and bacteria 8 , suggesting that the subjective Explicate Order controls both organismal behavior and trans‐generational evolution.

That perspective offers insight to the nature of consciousness: not as a “mind” that is separate from a “body”, but as an endogenization of physical matter, which complies with the Laws of Nature. In other words, consciousness is the physiologic manifestation of endogenized physical surroundings, compartmentalized, and made essential for all organisms by forming the basis for their physiology. Endocytosis and endocytic/synaptic vesicles contribute to endogenization of cellular surroundings, allowing eukaryotic organisms to gain knowledge about the environment. This is true not only for neurons in brains, but also for all eukaryotic cells 5 .

Such a view of consciousness offers insight to our awareness of our physical surroundings as the basis for self‐referential self‐organization. But this is predicated on our capacity to “experiment” with our environment. The burgeoning idea that we are entering the Anthropocene, a man‐made world founded on subjective senses instead of Natural Laws, is a dangerous step away from our innate evolutionary arc. Relying on just our senses and emotions, without experimentation and controls to understand the Implicate Order behind reality, is not just an abandonment of the principles of the Enlightenment, but also endangers the planet and its diversity of life.

Further reading

Anderson C (2008) The End of Theory: the data deluge makes the scientific method obsolete. Wired (December 23, 2008)

Bacon F (1620, 2011) Novum Organum Scientiarum. Nabu Press

Baluška F, Gagliano M, Witzany G (2018) Memory and Learning in Plants. Springer Nature

Charlesworth AG, Seroussi U, Claycomb JM (2019) Next‐Gen learning: the C. elegans approach. Cell 177: 1674–1676

Eliezer Y, Deshe N, Hoch L, Iwanir S, Pritz CO, Zaslaver A (2019) A memory circuit for coping with impending adversity. Curr Biol 29: 1573–1583

Gagliano M, Renton M, Depczynski M, Mancuso S (2014) Experience teaches plants to learn faster and forget slower in environments where it matters. Oecologia 175: 63–72

Gagliano M, Vyazovskiy VV, Borbély AA, Grimonprez M, Depczynski M (2016) Learning by association in plants. Sci Rep 6: 38427

Katz M, Shaham S (2019) Learning and memory: mind over matter in C. elegans . Curr Biol 29: R365‐R367

Kováč L (2007) Information and knowledge in biology – time for reappraisal. Plant Signal Behav 2: 65–73

Kováč L (2008) Bioenergetics – a key to brain and mind. Commun Integr Biol 1: 114–122

Koshland DE Jr (1980) Bacterial chemotaxis in relation to neurobiology. Annu Rev Neurosci 3: 43–75

Lyon P (2015) The cognitive cell: bacterial behavior reconsidered. Front Microbiol 6: 264

Margulis L (2001) The conscious cell. Ann NY Acad Sci 929: 55–70

Maximillian N (2018) The Metaphysics of Science and Aim‐Oriented Empiricism. Springer: New York

Mazzocchi F (2015) Could Big Data be the end of theory in science? EMBO Rep 16: 1250–1255

Moore RS, Kaletsky R, Murphy CT (2019) Piwi/PRG‐1 argonaute and TGF‐β mediate transgenerational learned pathogenic avoidance. Cell 177: 1827–1841

Peirce CS (1877) The Fixation of Belief. Popular Science Monthly 12: 1–15

Pigliucci M (2009) The end of theory in science? EMBO Rep 10: 534

Popper K (1959) The Logic of Scientific Discovery. Routledge: London

Posner R, Toker IA, Antonova O, Star E, Anava S, Azmon E, Hendricks M, Bracha S, Gingold H, Rechavi O (2019) Neuronal small RNAs control behavior transgenerationally. Cell 177: 1814–1826

Russell B (1912) The Problems of Philosophy. Henry Holt and Company: New York

Scerri E (2006) The Periodic Table: It's Story and Significance. Oxford University Press, Oxford

Shapiro JA (2007) Bacteria are small but not stupid: cognition, natural genetic engineering and socio‐bacteriology. Stud Hist Philos Biol Biomed Sci 38: 807–818

Torday JS, Miller WB Jr (2016) Biologic relativity: who is the observer and what is observed? Prog Biophys Mol Biol 121: 29–34

Torday JS, Rehan VK (2017) Evolution, the Logic of Biology. Wiley: Hoboken

Torday JS, Miller WB Jr (2016) Phenotype as agent for epigenetic inheritance. Biology (Basel) 5: 30

Wasserstein RL, Lazar NA (2016) The ASA's statement on p‐values: context, process and purpose. Am Statist 70: 129–133

Yamada T, Yang Y, Valnegri P, Juric I, Abnousi A, Markwalter KH, Guthrie AN, Godec A, Oldenborg A, Hu M, Holy TE, Bonni A (2019) Sensory experience remodels genome architecture in neural circuit to drive motor learning. Nature 569: 708–713

Ladislav Kováč discussed the advantages and drawbacks of the inductive method for science and the logic of scientific discoveries 9 . Obviously, technological advances have enabled scientists to expand the borders of knowledge, and informatics allows us to objectively analyze ever larger data‐sets. It was the telescope that enabled Tycho Brahe, Johannes Kepler, and Galileo Galilei to make accurate observations and infer the motion of the planets. The microscope provided Robert Koch and Louis Pasteur insights into the microbial world and determines the nature of infectious diseases. Particle colliders now give us a glimpse into the birth of the Universe, while DNA sequencing and bioinformatics have enormously advanced biology's goal to understand the molecular basis of life.

However, Kováč also reminds us that Bayesian inferences and reasoning have serious drawbacks, as documented in the instructive example of Bertrand Russell's “inductivist turkey”, which collected large amounts of reproducible data each morning about feeding time. Based on these observations, the turkey correctly predicted the feeding time for the next morning—until Christmas Eve when the turkey's throat was cut 9 . In order to avoid the fate of the “inductivist turkey”, mankind should also rely on Popperian deductive science, namely formulating theories, concepts, and hypotheses, which are either confirmed or refuted via stringent experimentation and proper controls. Even if our scientific instruments provide us with objective data, we still need to apply our consciousness to evaluate and interpret such data. Moreover, before we start using our scientific instruments, we need to pose scientific questions. Therefore, as suggested by Albert Szent‐Györgyi, we need both Dionysian and Apollonian types of scientists 10 . Unfortunately, as was the case in Szent‐Györgyi's times, the Dionysians are still struggling to get proper support.

There have been pleas for reconciling philosophy and science, which parted ways owing to the rise of empiricism. This essay recognizes the centrality experiments and their controls for the advancement of scientific thought, and the attendant advance in philosophy needed to cope with many extant and emerging issues in science and society. We need a common “will” to do so. The rationale is provided herein, if only.

Acknowledgements

John Torday has been a recipient of NIH Grant HL055268. František Baluška is thankful to numerous colleagues for very stimulating discussions on topics analyzed in this article.

EMBO Reports (2019) 20 : e49110 [ PMC free article ] [ PubMed ] [ Google Scholar ]

Contributor Information

John S Torday, Email: ude.alcu@yadrotj .

František Baluška, Email: ed.nnob-inu@aksulab .

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Control Group Definition and Examples

The control group is the set of subjects that does not receive the treatment in a study. In other words, it is the group where the independent variable is held constant. This is important because the control group is a baseline for measuring the effects of a treatment in an experiment or study. A controlled experiment is one which includes one or more control groups.

• The experimental group experiences a treatment or change in the independent variable. In contrast, the independent variable is constant in the control group.
• A control group is important because it allows meaningful comparison. The researcher compares the experimental group to it to assess whether or not there is a relationship between the independent and dependent variable and the magnitude of the effect.
• There are different types of control groups. A controlled experiment has one more control group.

Control Group vs Experimental Group

The only difference between the control group and experimental group is that subjects in the experimental group receive the treatment being studied, while participants in the control group do not. Otherwise, all other variables between the two groups are the same.

Control Group vs Control Variable

A control group is not the same thing as a control variable. A control variable or controlled variable is any factor that is held constant during an experiment. Examples of common control variables include temperature, duration, and sample size. The control variables are the same for both the control and experimental groups.

Types of Control Groups

There are different types of control groups:

• Placebo group : A placebo group receives a placebo , which is a fake treatment that resembles the treatment in every respect except for the active ingredient. Both the placebo and treatment may contain inactive ingredients that produce side effects. Without a placebo group, these effects might be attributed to the treatment.
• Positive control group : A positive control group has conditions that guarantee a positive test result. The positive control group demonstrates an experiment is capable of producing a positive result. Positive controls help researchers identify problems with an experiment.
• Negative control group : A negative control group consists of subjects that are not exposed to a treatment. For example, in an experiment looking at the effect of fertilizer on plant growth, the negative control group receives no fertilizer.
• Natural control group : A natural control group usually is a set of subjects who naturally differ from the experimental group. For example, if you compare the effects of a treatment on women who have had children, the natural control group includes women who have not had children. Non-smokers are a natural control group in comparison to smokers.
• Randomized control group : The subjects in a randomized control group are randomly selected from a larger pool of subjects. Often, subjects are randomly assigned to either the control or experimental group. Randomization reduces bias in an experiment. There are different methods of randomly assigning test subjects.

Control Group Examples

Here are some examples of different control groups in action:

Negative Control and Placebo Group

For example, consider a study of a new cancer drug. The experimental group receives the drug. The placebo group receives a placebo, which contains the same ingredients as the drug formulation, minus the active ingredient. The negative control group receives no treatment. The reason for including the negative group is because the placebo group experiences some level of placebo effect, which is a response to experiencing some form of false treatment.

Positive and Negative Controls

For example, consider an experiment looking at whether a new drug kills bacteria. The experimental group exposes bacterial cultures to the drug. If the group survives, the drug is ineffective. If the group dies, the drug is effective.

The positive control group has a culture of bacteria that carry a drug resistance gene. If the bacteria survive drug exposure (as intended), then it shows the growth medium and conditions allow bacterial growth. If the positive control group dies, it indicates a problem with the experimental conditions. A negative control group of bacteria lacking drug resistance should die. If the negative control group survives, something is wrong with the experimental conditions.

• Bailey, R. A. (2008).  Design of Comparative Experiments . Cambridge University Press. ISBN 978-0-521-68357-9.
• Chaplin, S. (2006). “The placebo response: an important part of treatment”.  Prescriber . 17 (5): 16–22. doi: 10.1002/psb.344
• Hinkelmann, Klaus; Kempthorne, Oscar (2008).  Design and Analysis of Experiments, Volume I: Introduction to Experimental Design  (2nd ed.). Wiley. ISBN 978-0-471-72756-9.
• Pithon, M.M. (2013). “Importance of the control group in scientific research.” Dental Press J Orthod . 18 (6):13-14. doi: 10.1590/s2176-94512013000600003
• Stigler, Stephen M. (1992). “A Historical View of Statistical Concepts in Psychology and Educational Research”. American Journal of Education . 101 (1): 60–70. doi: 10.1086/444032

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• 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.

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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

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.

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control group

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• Verywell Mind - What Is a Control Group?
• National Center for Biotechnology Information - PubMed Central - Control Group Design: Enhancing Rigor in Research of Mind-Body Therapies for Depression

control group , the standard to which comparisons are made in an experiment. Many experiments are designed to include a control group and one or more experimental groups; in fact, some scholars reserve the term experiment for study designs that include a control group. Ideally, the control group and the experimental groups are identical in every way except that the experimental groups are subjected to treatments or interventions believed to have an effect on the outcome of interest while the control group is not. Inclusion of a control group greatly strengthens researchers’ ability to draw conclusions from a study. Indeed, only in the presence of a control group can a researcher determine whether a treatment under investigation truly has a significant effect on an experimental group, and the possibility of making an erroneous conclusion is reduced. See also scientific method .

A typical use of a control group is in an experiment in which the effect of a treatment is unknown and comparisons between the control group and the experimental group are used to measure the effect of the treatment. For instance, in a pharmaceutical study to determine the effectiveness of a new drug on the treatment of migraines , the experimental group will be administered the new drug and the control group will be administered a placebo (a drug that is inert, or assumed to have no effect). Each group is then given the same questionnaire and asked to rate the effectiveness of the drug in relieving symptoms . If the new drug is effective, the experimental group is expected to have a significantly better response to it than the control group. Another possible design is to include several experimental groups, each of which is given a different dosage of the new drug, plus one control group. In this design, the analyst will compare results from each of the experimental groups to the control group. This type of experiment allows the researcher to determine not only if the drug is effective but also the effectiveness of different dosages. In the absence of a control group, the researcher’s ability to draw conclusions about the new drug is greatly weakened, due to the placebo effect and other threats to validity. Comparisons between the experimental groups with different dosages can be made without including a control group, but there is no way to know if any of the dosages of the new drug are more or less effective than the placebo.

It is important that every aspect of the experimental environment be as alike as possible for all subjects in the experiment. If conditions are different for the experimental and control groups, it is impossible to know whether differences between groups are actually due to the difference in treatments or to the difference in environment. For example, in the new migraine drug study, it would be a poor study design to administer the questionnaire to the experimental group in a hospital setting while asking the control group to complete it at home. Such a study could lead to a misleading conclusion, because differences in responses between the experimental and control groups could have been due to the effect of the drug or could have been due to the conditions under which the data were collected. For instance, perhaps the experimental group received better instructions or was more motivated by being in the hospital setting to give accurate responses than the control group.

In non-laboratory and nonclinical experiments, such as field experiments in ecology or economics , even well-designed experiments are subject to numerous and complex variables that cannot always be managed across the control group and experimental groups. Randomization, in which individuals or groups of individuals are randomly assigned to the treatment and control groups, is an important tool to eliminate selection bias and can aid in disentangling the effects of the experimental treatment from other confounding factors. Appropriate sample sizes are also important.

A control group study can be managed in two different ways. In a single-blind study, the researcher will know whether a particular subject is in the control group, but the subject will not know. In a double-blind study , neither the subject nor the researcher will know which treatment the subject is receiving. In many cases, a double-blind study is preferable to a single-blind study, since the researcher cannot inadvertently affect the results or their interpretation by treating a control subject differently from an experimental subject.