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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Experimental Design: Types, Examples & Methods
Saul McLeod, PhD
Editor-in-Chief for Simply Psychology
BSc (Hons) Psychology, MRes, PhD, University of Manchester
Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.
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Olivia Guy-Evans, MSc
Associate Editor for Simply Psychology
BSc (Hons) Psychology, MSc Psychology of Education
Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.
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Experimental design refers to how participants are allocated to different groups in an experiment. Types of design include repeated measures, independent groups, and matched pairs designs.
Probably the most common way to design an experiment in psychology is to divide the participants into two groups, the experimental group and the control group, and then introduce a change to the experimental group, not the control group.
The researcher must decide how he/she will allocate their sample to the different experimental groups. For example, if there are 10 participants, will all 10 participants participate in both groups (e.g., repeated measures), or will the participants be split in half and take part in only one group each?
Three types of experimental designs are commonly used:
1. Independent Measures
Independent measures design, also known as between-groups , is an experimental design where different participants are used in each condition of the independent variable. This means that each condition of the experiment includes a different group of participants.
This should be done by random allocation, ensuring that each participant has an equal chance of being assigned to one group.
Independent measures involve using two separate groups of participants, one in each condition. For example:
- Con : More people are needed than with the repeated measures design (i.e., more time-consuming).
- Pro : Avoids order effects (such as practice or fatigue) as people participate in one condition only. If a person is involved in several conditions, they may become bored, tired, and fed up by the time they come to the second condition or become wise to the requirements of the experiment!
- Con : Differences between participants in the groups may affect results, for example, variations in age, gender, or social background. These differences are known as participant variables (i.e., a type of extraneous variable ).
- Control : After the participants have been recruited, they should be randomly assigned to their groups. This should ensure the groups are similar, on average (reducing participant variables).
2. Repeated Measures Design
Repeated Measures design is an experimental design where the same participants participate in each independent variable condition. This means that each experiment condition includes the same group of participants.
Repeated Measures design is also known as within-groups or within-subjects design .
- Pro : As the same participants are used in each condition, participant variables (i.e., individual differences) are reduced.
- Con : There may be order effects. Order effects refer to the order of the conditions affecting the participants’ behavior. Performance in the second condition may be better because the participants know what to do (i.e., practice effect). Or their performance might be worse in the second condition because they are tired (i.e., fatigue effect). This limitation can be controlled using counterbalancing.
- Pro : Fewer people are needed as they participate in all conditions (i.e., saves time).
- Control : To combat order effects, the researcher counter-balances the order of the conditions for the participants. Alternating the order in which participants perform in different conditions of an experiment.
Counterbalancing
Suppose we used a repeated measures design in which all of the participants first learned words in “loud noise” and then learned them in “no noise.”
We expect the participants to learn better in “no noise” because of order effects, such as practice. However, a researcher can control for order effects using counterbalancing.
The sample would be split into two groups: experimental (A) and control (B). For example, group 1 does ‘A’ then ‘B,’ and group 2 does ‘B’ then ‘A.’ This is to eliminate order effects.
Although order effects occur for each participant, they balance each other out in the results because they occur equally in both groups.
3. Matched Pairs Design
A matched pairs design is an experimental design where pairs of participants are matched in terms of key variables, such as age or socioeconomic status. One member of each pair is then placed into the experimental group and the other member into the control group .
One member of each matched pair must be randomly assigned to the experimental group and the other to the control group.
- Con : If one participant drops out, you lose 2 PPs’ data.
- Pro : Reduces participant variables because the researcher has tried to pair up the participants so that each condition has people with similar abilities and characteristics.
- Con : Very time-consuming trying to find closely matched pairs.
- Pro : It avoids order effects, so counterbalancing is not necessary.
- Con : Impossible to match people exactly unless they are identical twins!
- Control : Members of each pair should be randomly assigned to conditions. However, this does not solve all these problems.
Experimental design refers to how participants are allocated to an experiment’s different conditions (or IV levels). There are three types:
1. Independent measures / between-groups : Different participants are used in each condition of the independent variable.
2. Repeated measures /within groups : The same participants take part in each condition of the independent variable.
3. Matched pairs : Each condition uses different participants, but they are matched in terms of important characteristics, e.g., gender, age, intelligence, etc.
Learning Check
Read about each of the experiments below. For each experiment, identify (1) which experimental design was used; and (2) why the researcher might have used that design.
1 . To compare the effectiveness of two different types of therapy for depression, depressed patients were assigned to receive either cognitive therapy or behavior therapy for a 12-week period.
The researchers attempted to ensure that the patients in the two groups had similar severity of depressed symptoms by administering a standardized test of depression to each participant, then pairing them according to the severity of their symptoms.
2 . To assess the difference in reading comprehension between 7 and 9-year-olds, a researcher recruited each group from a local primary school. They were given the same passage of text to read and then asked a series of questions to assess their understanding.
3 . To assess the effectiveness of two different ways of teaching reading, a group of 5-year-olds was recruited from a primary school. Their level of reading ability was assessed, and then they were taught using scheme one for 20 weeks.
At the end of this period, their reading was reassessed, and a reading improvement score was calculated. They were then taught using scheme two for a further 20 weeks, and another reading improvement score for this period was calculated. The reading improvement scores for each child were then compared.
4 . To assess the effect of the organization on recall, a researcher randomly assigned student volunteers to two conditions.
Condition one attempted to recall a list of words that were organized into meaningful categories; condition two attempted to recall the same words, randomly grouped on the page.
Experiment Terminology
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 which 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 taking part 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.
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.
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