Control Group vs Experimental Group: Differences, Examples and Real Data

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Quick answerThe experimental group receives the treatment or change you are testing. The control group is as similar as possible but does not receive it, so it shows what would have happened anyway. Comparing the two tells you whether the treatment made a difference.

Below, we explain both groups in plain language, show why random assignment matters, and look at how often controlled trials actually appear in the published work of PhD researchers on ScholarsColab.

Control group vs experimental group at a glance

Experimental group Control group
What it gets The treatment, intervention or change being tested No treatment, a placebo, or the usual standard
Purpose Shows the outcome with the treatment Shows the outcome without it, as a baseline
Also called Treatment group, intervention group Comparison group, placebo group, baseline
Example Patients given a new drug Patients given a placebo tablet
Key requirement Both groups must be alike in every important way except the treatment

Why the control group matters

Imagine students take a new revision course and their exam scores rise. Did the course work? Scores might have risen anyway: the students studied more as exams approached, or the exam was easier this year. Without a group that did not take the course, you cannot tell. The control group answers the question “compared with what?”.

The difference between the two groups’ outcomes is the effect of the treatment, but only if the groups were alike at the start. That is why researchers assign people to the groups at random. Randomisation spreads age, ability, health and countless unknown factors evenly across both groups, so any difference at the end can be traced to the treatment.

Remember: the control group is not “the group where nothing happens”. It is the group that experiences everything the experimental group does, except the one thing you are testing.

Example: control and experimental groups in our mentors’ research

To explain these two groups, we use real studies published by PhD researchers on ScholarsColab. Our data: 41,792 papers, mostly published between 1998 and 2026, by 967 ScholarsColab mentors (data updated 15 October 2026). We will read a few of these studies step by step and find the two groups in each.

Step 1
Pick a real study that tests something. The papers below are controlled trials published by our mentors (the selection changes every day). Each one tests whether a treatment, food or procedure makes a difference.

Published paperYearCitations
Exploring Pharmacological Mechanisms, Clinical Trials and Patent Landscapes of Berberine: A Review
Dr Geeta Deswal		20242
Some Preliminary Observations from a Randomized Trial of Cervical Visual Screening in Dindigul District in Rural South India
Dr Prof Rajkumar Rajamanickam		20060
An integrated systematic review and meta-analysis of published randomized controlled trials evaluating nasogastric against postpyloris feeding in critically ill surgical and …
Prof Kishan Pal Singh		20130
Treatment of non‐alcoholic fatty liver disease with curcumin: A randomized placebo‐controlled trial
Amirhossein Sahebkar		2016518
Safety and effectiveness of high-dose vitamin C in patients with COVID-19: a randomized open-label clinical trial
SeyedAhmad SeyedAlinaghi		2021184

Step 2
Find the experimental group: who received the thing being tested? In a study of a new eye operation, it is the patients who had the new operation. In a study of a supplement, it is the people who took the supplement.

Step 3
Find the control group: who did not? These are the patients who had the usual operation, or the people who took a dummy tablet (a placebo). They show what happens without the new treatment.

Experimental groupgets the new treatment
vs
Control groupusual treatment or placebo

Step 4
Compare the results. If the experimental group improves more than the control group, and the two groups were alike at the start, the difference can be credited to the treatment.

Step 5
Notice how rare true trials are. 77 of the 41,792 papers in our data (0.2%) show this in their title: randomised / controlled trial. It is most common in Medicine & health, where 0.8% of papers show it. Trials with real patients are expensive, so they are concentrated in medicine. Researchers in engineering and computer science, who make up much of our data, more often test ideas with laboratory experiments and computer simulations, which still compare a changed version against an unchanged one.

Papers by methodSimulation / modelling7381.8% of all papersExperimental / laboratory study3560.9% of all papersRandomised / controlled trial770.2% of all papers
Number of papers whose title names each method or design (a paper can name more than one). Source: ScholarsColab Research Methods Index.

Types of control group

Type What the control group receives Typical use
No-treatment control Nothing Early experiments where any effect is of interest
Placebo control A dummy treatment that looks the same Drug trials, where expectations alone can change outcomes
Active (standard) control The current best treatment When withholding treatment would be unethical
Waiting-list control The treatment later, after the study Education, therapy and training programmes
Historical control Nothing new: past records are used for comparison When a new group cannot be formed; weaker evidence

Examples across fields

Field Experimental group Control group
Medicine Patients receiving a new antihypertensive drug Patients receiving a placebo
Agriculture Plots treated with a biofertiliser Identical plots without it
Education Classes taught with a flipped-classroom method Classes taught as usual
Computer science Users shown a redesigned interface (variant B) Users shown the current interface (variant A)
Civil engineering Concrete samples with a new admixture Samples of the standard mix

How to set up control and experimental groups

  1. Define the one thing you are testing, and the outcome you will measure.
  2. Decide what the control group receives: nothing, a placebo or the current standard.
  3. Assign participants or samples at random, so the groups start out alike.
  4. Treat both groups identically apart from the treatment: same timing, same measurements, same conditions.
  5. Blind where you can, so participants and assessors do not know who received what.
  6. Compare the outcomes with a suitable statistical test.

Common mistakes reviewers point out

  • No control group at all, so an improvement cannot be separated from natural change.
  • Groups that differ from the start, for example volunteers in one group and non-volunteers in the other.
  • Treating groups differently in other ways, such as measuring them at different times.
  • Too few participants to detect a realistic effect.
  • Calling a before-and-after comparison a controlled experiment. Measuring the same group twice is not the same as having a control group.

Frequently asked questions

What is a control group in an experiment?

It is the group that does not receive the treatment being tested, or receives a placebo or the standard treatment instead. It shows what happens without the treatment, so its outcome can be compared with the experimental group.

What is an experimental group?

It is the group that receives the treatment or change being tested. Its outcome is compared with the control group to see whether the treatment had an effect.

Why are participants assigned to groups at random?

Random assignment makes the groups alike in age, ability, health and other factors, including ones the researcher has not thought of. Any difference in outcome can then be attributed to the treatment rather than to differences between the groups.

Can a study have more than one experimental group?

Yes. Many studies test several doses or versions of a treatment, each with its own experimental group, and compare them all with one control group.

Is a placebo group the same as a control group?

A placebo group is one kind of control group. It receives a dummy treatment that looks like the real one, which separates the effect of the treatment from the effect of people’s expectations.

Use this data in your own work

Use this data in your own research. Download the aggregated figures behind these charts (counts, medians and shares by type of paper, period, method and discipline) as a CSV file. Please cite it as:ScholarsColab (2026). ScholarsColab Research Methods Index [Data set]. Vidhilekha Soft Solutions Pvt Ltd. Retrieved 15 October 2026, from https://scholarscolab.com/research-methodology/. Licensed under CC BY 4.0.Download the data (CSV) →

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About the data on this page. The ScholarsColab Research Methods Index is built from the Google Scholar publication lists of PhD researchers who mentor on ScholarsColab, and is updated daily as mentors and publications are added. Methods, designs and paper types are identified from titles, so papers whose titles do not mention them are not counted in those groups. The mentors are not a random sample of all researchers (engineering and computer science are strongly represented), so the figures describe this group of researchers and may not apply to research as a whole.