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The Difference Between Control Group and Experimental Group

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In an experiment , data from an experimental group is compared with data from a control group. These two groups should be identical in every respect except one: the difference between a control group and an experimental group is that the independent variable is changed for the experimental group, but is held constant in the control group.

Key Takeaways: Control vs. Experimental Group

• The control group and experimental group are compared against each other in an experiment. The only difference between the two groups is that the independent variable is changed in the experimental group. The independent variable is "controlled", or held constant, in the control group.
• A single experiment may include multiple experimental groups, which may all be compared against the control group.
• The purpose of having a control is to rule out other factors which may influence the results of an experiment. Not all experiments include a control group, but those that do are called "controlled experiments."
• A placebo may also be used in an experiment. A placebo isn't a substitute for a control group because subjects exposed to a placebo may experience effects from the belief they are being tested; this itself is known as the placebo effect.

What Are Is an Experimental Group in Experiment Design?

An experimental group is a test sample or the group that receives an experimental procedure. This group is exposed to changes in the independent variable being tested. The values of the independent variable and the impact on the dependent variable are recorded. An experiment may include multiple experimental groups at one time.

A control group is a group separated from the rest of the experiment such that 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.

While all experiments have an experimental group, not all experiments require a control group. Controls are extremely useful where the experimental conditions are complex and difficult to isolate. Experiments that use control groups are called controlled experiments .

A Simple Example of a Controlled Experiment

A simple example of a controlled experiment may be used to determine whether or not plants need to be watered to live. The control group would be plants that are not watered. The experimental group would consist of plants that receive water. A clever scientist would wonder whether too much watering might kill the plants and would set up several experimental groups, each receiving a different amount of water.

Sometimes setting up a controlled experiment can be confusing. For example, a scientist may wonder whether or not a species of bacteria needs oxygen in order to live. To test this, cultures of bacteria may be left in the air, while other cultures are placed in a sealed container of nitrogen (the most common component of air) or deoxygenated air (which likely contained extra carbon dioxide). Which container is the control? Which is the experimental group?

Control Groups and Placebos

The most common type of control group is one held at ordinary conditions so it doesn't experience a changing variable. For example, If you want to explore the effect of salt on plant growth, the control group would be a set of plants not exposed to salt, while the experimental group would receive the salt treatment. If you want to test whether the duration of light exposure affects fish reproduction, the control group would be exposed to a "normal" number of hours of light, while the duration would change for the experimental group.

Experiments involving human subjects can be much more complex. If you're testing whether a drug is effective or not, for example, members of a control group may expect they will not be unaffected. To prevent skewing the results, a placebo may be used. A placebo is a substance that doesn't contain an active therapeutic agent. If a control group takes a placebo, participants don't know whether they are being treated or not, so they have the same expectations as members of the experimental group.

However, there is also the placebo effect to consider. Here, the recipient of the placebo experiences an effect or improvement because she believes there should be an effect. Another concern with a placebo is that it's not always easy to formulate one that truly free of active ingredients. For example, if a sugar pill is given as a placebo, there's a chance the sugar will affect the outcome of the experiment.

Positive and Negative Controls

Positive and negative controls are two other types of control groups:

• Positive control groups are control groups in which the conditions guarantee a positive result. Positive control groups are effective to show the experiment is functioning as planned.
• Negative control groups are control groups in which conditions produce a negative outcome. Negative control groups help identify outside influences which may be present that were not unaccounted for, such as contaminants.
• 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 : 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.
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Control Group vs. Experimental Group

What's the difference.

Control group and experimental group are two essential components of a scientific experiment. The control group serves as a baseline for comparison, as it does not receive any treatment or intervention. It helps researchers determine the natural or expected outcome of the experiment. On the other hand, the experimental group is exposed to the independent variable or the treatment being tested. By comparing the results of the control group with the experimental group, researchers can assess the effectiveness or impact of the treatment. The control group provides a reference point, while the experimental group allows for the evaluation of the specific variable being studied.

Further Detail

Introduction.

In scientific research, control groups and experimental groups play crucial roles in understanding the effects of variables and determining causality. These groups are essential in conducting experiments and studies to gather reliable data and draw meaningful conclusions. While both groups serve distinct purposes, they possess different attributes that set them apart. In this article, we will explore and compare the attributes of control groups and experimental groups, shedding light on their significance in research.

Control Group

A control group is a group of individuals or subjects in an experiment that does not receive the experimental treatment or intervention. It serves as a baseline against which the experimental group is compared. The primary purpose of a control group is to provide a reference point to measure the effects of the independent variable in the experimental group. By keeping all other variables constant, except for the one being tested, researchers can determine whether the observed changes are due to the intervention or other factors.

One attribute of a control group is that it is randomly selected or assigned. Randomization helps ensure that the control group represents the larger population accurately, reducing the potential for bias. Additionally, the control group should be similar to the experimental group in terms of relevant characteristics such as age, gender, and health status. This similarity allows for a more accurate comparison between the two groups.

Another attribute of a control group is that it receives a placebo or a standard treatment. Placebos are inert substances or procedures that mimic the experimental treatment but have no therapeutic effect. By providing a placebo to the control group, researchers can account for the placebo effect, where individuals may experience improvements simply due to their belief in receiving treatment. Alternatively, the control group may receive a standard treatment that is already established as effective, allowing researchers to compare the experimental treatment against an existing standard.

Control groups are also characterized by their size. The larger the control group, the more reliable the results are likely to be. A larger sample size helps reduce the impact of individual variations and increases the statistical power of the study. It allows for more accurate generalizations and strengthens the validity of the findings.

Lastly, control groups are typically subjected to the same conditions as the experimental group, except for the intervention being tested. This ensures that any observed differences between the two groups can be attributed to the independent variable and not external factors. By controlling the environment and other variables, researchers can isolate the effects of the intervention and draw more accurate conclusions.

Experimental Group

The experimental group, also known as the treatment group, is the group of individuals or subjects in an experiment that receives the experimental treatment or intervention being tested. Unlike the control group, the experimental group is exposed to the independent variable, allowing researchers to assess the effects of the intervention.

One attribute of the experimental group is that it is carefully selected or assigned. Researchers must ensure that the individuals in the experimental group meet specific criteria and are representative of the population being studied. This selection process helps increase the internal validity of the study and enhances the generalizability of the findings.

Another attribute of the experimental group is that it undergoes the experimental treatment or intervention. This treatment can be a new drug, therapy, educational program, or any other intervention being tested. By administering the intervention to the experimental group, researchers can observe and measure its effects, comparing them to the control group's outcomes.

The size of the experimental group is also an important attribute. Similar to the control group, a larger sample size in the experimental group increases the reliability and statistical power of the study. It allows for more accurate assessments of the intervention's effectiveness and helps identify any potential side effects or adverse reactions.

Experimental groups are often subjected to pre and post-tests to measure the changes resulting from the intervention. These tests can include surveys, physical examinations, cognitive assessments, or any other relevant measurements. By comparing the pre and post-intervention results, researchers can determine the impact of the intervention on the dependent variable.

Lastly, experimental groups may be divided into subgroups to explore different variables or conditions. This approach allows researchers to assess the effects of the intervention across various demographics, such as age groups or different levels of severity. By analyzing subgroups within the experimental group, researchers can gain a deeper understanding of how the intervention affects different populations.

Control groups and experimental groups are fundamental components of scientific research. While control groups provide a reference point and help establish causality, experimental groups allow researchers to assess the effects of interventions. Both groups possess distinct attributes that contribute to the validity and reliability of the study. By understanding and comparing the attributes of control groups and experimental groups, researchers can conduct rigorous experiments and generate meaningful insights that advance scientific knowledge.

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What Is a Control Group?

Control Groups vs. Experimental Groups in Psychology Research

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

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

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Control Group vs. Experimental Group

Types of control groups.

In simple terms, the control group comprises participants who do not receive the experimental treatment. When conducting an experiment, these people are randomly assigned to this group. They also closely resemble the participants who are in the experimental group or the individuals who receive the treatment.

Experimenters utilize variables to make comparisons between an experimental group and a control group. A variable is something that researchers can manipulate, measure, and control in an experiment. The independent variable is the aspect of the experiment that the researchers manipulate (or the treatment). The dependent variable is what the researchers measure to see if the independent variable had an effect.

While they do not receive the treatment, the control group does play a vital role in the research process. Experimenters compare the experimental group to the control group to determine if the treatment had an effect.

By serving as a comparison group, researchers can isolate the independent variable and look at the impact it had.

The simplest way to determine the difference between a control group and an experimental group is to determine which group receives the treatment and which does not. To ensure that the results can then be compared accurately, the two groups should be otherwise identical.

Not exposed to the treatment (the independent variable)

Used to provide a baseline to compare results against

May receive a placebo treatment

Exposed to the treatment

Used to measure the effects of the independent variable

Identical to the control group aside from their exposure to the treatment

Why a Control Group Is Important

While the control group does not receive treatment, it does play a critical role in the experimental process. This group serves as a benchmark, allowing researchers to compare the experimental group to the control group to see what sort of impact changes to the independent variable produced.  

Because participants have been randomly assigned to either the control group or the experimental group, it can be assumed that the groups are comparable.

Any differences between the two groups are, therefore, the result of the manipulations of the independent variable. The experimenters carry out the exact same procedures with both groups with the exception of the manipulation of the independent variable in the experimental group.

There are a number of different types of control groups that might be utilized in psychology research. Some of these include:

• Positive control groups : In this case, researchers already know that a treatment is effective but want to learn more about the impact of variations of the treatment. In this case, the control group receives the treatment that is known to work, while the experimental group receives the variation so that researchers can learn more about how it performs and compares to the control.
• Negative control group : In this type of control group, the participants are not given a treatment. The experimental group can then be compared to the group that did not experience any change or results.
• Placebo control group : This type of control group receives a placebo treatment that they believe will have an effect. This control group allows researchers to examine the impact of the placebo effect and how the experimental treatment compared to the placebo treatment.
• Randomized control group : This type of control group involves using random selection to help ensure that the participants in the control group accurately reflect the demographics of the larger population.
• Natural control group : This type of control group is naturally selected, often by situational factors. For example, researchers might compare people who have experienced trauma due to war to people who have not experienced war. The people who have not experienced war-related trauma would be the control group.

Examples of Control Groups

Control groups can be used in a variety of situations. For example, imagine a study in which researchers example how distractions during an exam influence test results. The control group would take an exam in a setting with no distractions, while the experimental groups would be exposed to different distractions. The results of the exam would then be compared to see the effects that distractions had on test scores.

Experiments that look at the effects of medications on certain conditions are also examples of how a control group can be used in research. For example, researchers looking at the effectiveness of a new antidepressant might use a control group that receives a placebo and an experimental group that receives the new medication. At the end of the study, researchers would compare measures of depression for both groups to determine what impact the new medication had.

After the experiment is complete, researchers can then look at the test results and start making comparisons between the control group and the experimental group.

Uses for Control Groups

Researchers utilize control groups to conduct research in a range of different fields. Some common uses include:

• Psychology : Researchers utilize control groups to learn more about mental health, behaviors, and treatments.
• Medicine : Control groups can be used to learn more about certain health conditions, assess how well medications work to treat these conditions, and assess potential side effects that may result.
• Education : Educational researchers utilize control groups to learn more about how different curriculums, programs, or instructional methods impact student outcomes.
• Marketing : Researchers utilize control groups to learn more about how consumers respond to advertising and marketing efforts.

Malay S, Chung KC. The choice of controls for providing validity and evidence in clinical research . Plast Reconstr Surg. 2012 Oct;130(4):959-965. doi:10.1097/PRS.0b013e318262f4c8

National Cancer Institute. Control group.

Pithon MM. Importance of the control group in scientific research . Dental Press J Orthod. 2013;18(6):13-14. doi:10.1590/s2176-94512013000600003

Karlsson P, Bergmark A. Compared with what? An analysis of control-group types in Cochrane and Campbell reviews of psychosocial treatment efficacy with substance use disorders . Addiction . 2015;110(3):420-8. doi:10.1111/add.12799

Myers A, Hansen C. Experimental Psychology . Belmont, CA: Cengage Learning; 2012.

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

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• Control Groups and Treatment Groups | Uses & Examples

Control Groups & Treatment Groups | Uses & Examples

Published on 6 May 2022 by Lauren Thomas . Revised on 13 April 2023.

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

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.

Table of contents

Control groups in experiments, control groups in non-experimental research, importance of control groups, 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).

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 randomisation 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 region but not in the neighbouring region.

In these cases, the classes that did not use the new teaching method, or the region 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.

Minimising this risk

A few methods can aid you in minimising 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 minimise the differences between the two groups on confounding variables that you can directly observe, but also those you cannot.

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 (aka 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.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment
• Random assignment of participants to ensure the groups are equivalent

Depending on your study topic, there are various other methods of controlling 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.

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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.