In a controlled experiment, scientists compare a control group and an experimental group that are identical in every respect 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.
Experimental manipulation is the only difference between the two groups. So we can be confident that any differences between them reflect that manipulation, not 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.
Random assignment makes this possible. Because participants are randomly assigned to either group, we can assume the groups are identical except for the variable manipulated in the experimental group.
Every aspect of the experimental environment must stay identical for both groups, and experimenters must follow the exact same procedures with each one. That way, researchers can be confident that any differences between groups reflect the treatment itself, not some other factor.
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, and ethnicity.
The difference between the control and experimental groups is that the control group is not exposed to the independent variable. That variable is thought to cause 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 serves as a baseline. This lets researchers see what impact changes to the independent variable produce, and draw firmer conclusions from a study.
Without a control group, the picture is incomplete. A researcher cannot determine whether a particular treatment truly affected the experimental group.
Control groups are critical to the scientific method as they help ensure a study’s internal validity. Internal validity is the confidence that the independent variable, not some other factor, produced the result.
Example
Assume you want to test a new medication for ADHD. Researchers split participants into two groups.
One group receives the new medication. The other group receives a placebo: a pill that looks identical but contains no active ingredient.
The placebo group is the control group. It exists for a reason. People often feel better simply from believing they have taken a treatment, not from the treatment itself.
Comparing the medication group against the placebo group, rather than against no treatment at all, lets researchers isolate the drug’s real effect from participants’ expectations.
Beecher’s classic study shows why.
Aim: Henry Beecher, an anaesthesiologist, wanted to find out how large and how reliable placebo responses really were across medical practice.
Method: Beecher (1955) reviewed 15 published clinical studies in which patients had received a placebo instead of an active treatment, across conditions including post-operative pain, seasickness, and anxiety.
Results: On average, placebos helped about 35% of patients. That proportion was comparable, in some studies, to a meaningful fraction of the active drug’s own effect.
Conclusion: Beecher concluded that the placebo response was a real, measurable phenomenon, not simply “no effect,” and used the finding to argue for including placebo control groups in clinical trials.
Beecher’s 35% figure has since been challenged. Even so, his core argument still holds: an apparent treatment effect cannot be trusted without a placebo comparison.
Types of Control Groups
Positive Control Group
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- Definition: A positive control group is an experimental condition expected to produce a known effect, confirming that the experimental procedure itself is capable of detecting a real effect.
- Purpose: It rules out the possibility that a “no effect” result reflects an insensitive experimental setup rather than a genuinely ineffective treatment.
- Example: When testing a new medication, an already licensed medication can serve as the positive control.
Negative Control Group
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- Definition: A negative control group is an experimental condition expected to produce no effect, confirming that any change seen elsewhere is not simply an artifact of the procedure.
- Purpose: It helps rule out factors other than the treatment, such as participants’ expectations, as the true cause of any observed change.
- Example: A placebo group in a medication trial commonly serves as the negative control.
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.
Example
Assume you want to find out whether listening to music affects focus while studying.
You randomly assign participants to one of three groups. One group listens to music with lyrics, one listens to music without lyrics, and the third listens to no music at all.
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.
Critical Evaluation of Control Groups
Control groups do valuable work, but they have real limits. The sections below weigh what control buys against what it costs.
Strengths of Control Groups
A control group is what makes a causal claim possible. Without a genuine baseline for comparison, an apparent effect of the treatment cannot be distinguished from a coincidence, a pre-existing group difference, or what would have happened anyway.
A simple before-and-after comparison cannot rule these out.
Consider a study that simply measures people before and after a new revision technique, then credits any improvement to the technique itself.
That improvement could equally reflect maturation, extra practice, or students who would have improved anyway, regardless of the technique. Random allocation and a control group rule out these rival explanations, which is why a well-controlled experiment supports a stronger cause-and-effect conclusion than a simple before-and-after study.
This is the whole toolkit’s payoff.
Limitations of Control
Randomisation has real limits too.
Krause and Howard (2003) showed that it removes systematic bias in how groups are formed, but does not guarantee that any single study’s groups are actually equivalent.
With realistic sample sizes, chance imbalances between groups are common, so a randomised study can still compare two groups that differ on an unmeasured variable.
Tight control also has a cost.
The artificial conditions that make a laboratory experiment easy to control can make the situation participants encounter unlike anything in ordinary life. Participants often notice this and adjust their behaviour to what they infer the situation demands, a pattern Martin Orne (1962) termed demand characteristics.
A tightly controlled study can trade away real-world relevance for its internal validity.
Contemporary Research
For decades, the case for control rested on logic. A newer body of research asks a more direct question: across real published trials, does using them actually change the result?
Aim: Page and colleagues (2016) aimed to synthesise the evidence on how far weak randomisation, poor allocation concealment, and lack of blinding bias the treatment effects trials report.
Their method was a synthesis of syntheses.
Method: They combined 24 existing meta-epidemiological studies, each of which had already compared trial results with and without a given design feature. They then compared subjective outcomes, such as pain, against objective ones, such as mortality.
Results: Trials with unclear randomisation or allocation concealment exaggerated effects by roughly 7 to 10% on average.
Blinding mattered far more.
Unblinded trials exaggerated subjective outcomes, such as pain ratings, by around 23%, while objective outcomes like mortality showed little to no such exaggeration.
Conclusion: Page and colleagues concluded that specific design flaws, especially inadequate blinding, inflate reported treatment effects, particularly for subjectively judged outcomes.
The pattern goes back further than 2016.
A large project replicated 100 studies from three major psychology journals and found that only 36% reproduced the original result, with effect sizes roughly halved (Open Science Collaboration, 2015). In response, researchers proposed a package of reforms, including pre-registration, larger samples, and routine independent replication (Munafò et al., 2017).
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.
Key Takeaways
- Baseline Comparison: The control group is not exposed to the independent variable, so it shows what would have happened without the treatment.
- Random Allocation: Assigning participants to groups by chance spreads individual differences evenly, rather than matching people one by one.
- Positive vs Negative: A positive control confirms the procedure can detect a real effect; a negative control confirms nothing appears when it shouldn’t.
- Placebo Control: An inactive but identical-looking treatment isolates participants’ belief in the treatment from the treatment’s real, active effect.
- Modern Evidence: Large-scale reviews show that trials without proper randomisation or blinding report inflated effects, especially for subjectively judged outcomes.
References
Bailey, R. A. (2008). Design of Comparative Experiments. Cambridge University Press. ISBN 978-0-521-68357-9.
Bailey, R. A. (2008). Design of Comparative Experiments. Cambridge University Press. ISBN 978-0-521-68357-9.
Beecher, H. K. (1955). The powerful placebo. Journal of the American Medical Association, 159(17), 1602–1606. https://doi.org/10.1001/jama.1955.02960340022006
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.
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.
Krause, M. S., & Howard, K. I. (2003). What random assignment does and does not do. Journal of Clinical Psychology, 59(7), 751–766. https://doi.org/10.1002/jclp.10170
Munafò, M. R., Nosek, B. A., Bishop, D. V. M., Button, K. S., Chambers, C. D., Percie du Sert, N., Simonsohn, U., Wagenmakers, E.-J., Ware, J. J., & Ioannidis, J. P. A. (2017). A manifesto for reproducible science. Nature Human Behaviour, 1, Article 0021. https://doi.org/10.1038/s41562-016-0021
Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251), aac4716. https://doi.org/10.1126/science.aac4716
Orne, M. T. (1962). On the social psychology of the psychological experiment: With particular reference to demand characteristics and their implications. American Psychologist, 17(11), 776–783. https://doi.org/10.1037/h0043424
Page, M. J., Higgins, J. P. T., Clayton, G., Sterne, J. A. C., Hróbjartsson, A., & Savović, J. (2016). Empirical evidence of study design biases in randomized trials: Systematic review of meta-epidemiological studies. PLOS ONE, 11(7), e0159267. https://doi.org/10.1371/journal.pone.0159267
