Control Groups in Experiments: How to Choose and Use Them

A control group is the set of subjects or samples in an experiment that does not receive the active treatment or intervention under investigation, serving as a baseline against which the effects of that treatment can be measured. For students, researchers, and life-science professionals, the control group answers a fundamental question: what would happen to the experimental group if the treatment were not applied? Without a properly selected control, you cannot determine whether an observed effect is caused by your intervention or by other factors such as natural variation, the act of being observed, or the laboratory environment. This article explains the types of control groups, how to choose among them, and how to implement them correctly in life-science research.

What a Control Group Does in an Experiment

The purpose of a control group is to provide a reference point that isolates the effect of the independent variable. In a typical experiment, you have at least two groups: the experimental group, which receives the treatment, and the control group, which does not. The control group is treated identically to the experimental group in every other way, so that any difference in outcomes can be attributed to the treatment instead of to confounding variables.

Quantitative research designs are broadly classified as either experimental or quasi-experimental, and the main distinguishing feature of the quasi-experiment is the manipulation of the independent variable without randomisation. When randomisation or use of a control group is unfeasible, a researcher can choose from a range of quasi-experimental designs. The non-equivalent control group post-test-only design aims to demonstrate causality between an intervention and an outcome, and it can be used in natural settings where randomisation cannot be conducted for ethical or practical reasons. Although this design is less complex than some others, with low error propagation, it is vulnerable to threats to internal validity.

In implementation science, many questions can be feasibly answered by fully experimental designs, typically in the form of randomized controlled trials. Implementation-focused randomized controlled trials usually differ from traditional efficacy or effectiveness trials on key parameters. Other implementation science questions are more suited to quasi-experimental designs, which are intended to estimate the effect of an intervention in the absence of randomization. These designs include pre-post designs with a non-equivalent control group, interrupted time series, and stepped wedges, the last of which require all participants to receive the intervention, but in a staggered fashion.

At a Glance: Control Group Types and When to Use Them

The table below summarizes the main types of control groups, what they control for, and when each is appropriate. Use this table as a starting point when designing your experiment.

Core Principles of Control Group Selection

Randomization and Its Role in Control

Randomization is the process of assigning subjects to experimental or control groups by chance. This reduces selection bias and increases the likelihood that the groups are comparable at the start of the study. When randomization is possible, it strengthens the internal validity of your experiment because differences between groups at the end of the study are more likely to be due to the treatment.

When randomization is not possible, you must use a quasi-experimental design. The non-equivalent control group post-test-only design is one option, but it is vulnerable to threats to internal validity because the groups may differ in important ways before the intervention begins. If you use this design, you should document the reasons randomization was not feasible and discuss the limitations in your interpretation of results.

The Control Group Must Be Treated Equally

A common error is to give the experimental group more attention, handling, or measurement time than the control group. This introduces a confounding variable because any difference in outcomes could be due to the extra attention instead of the treatment. In behavioral intervention trials, a well-designed control condition is an essential component to foster the unambiguous interpretation of study findings. Pitfalls in the design of control conditions include failing to match the amount of contact time between groups and failing to provide a credible alternative intervention.

The fall evaluation and prevention program used an active control condition to overcome limitations of previous trial designs. This control condition provided participants with an intervention that was matched for attention and contact time but did not contain the active components of the experimental intervention. This approach allowed the researchers to attribute differences in outcomes to the specific components of the experimental intervention instead of to the general effects of participating in a program.

Contamination and Dropout

Contamination occurs when control group members receive the experimental intervention or when experimental group members do not receive it. Differential dropout occurs when control group members leave the study at a different rate than experimental group members. Both problems can jeopardize the internal validity of your study.

In exercise oncology trials, important considerations are contamination and differential dropout among control group members. The lowest contamination and low dropout rates were found in control groups offered an intervention after the intervention period. When control groups were offered an intervention both during and after the intervention period, contamination was zero and excess dropout rates were low. These findings suggest that offering control group members something of value, either during or after the study, can reduce the likelihood that they will seek the experimental intervention on their own or drop out of the study.

Types of Control Groups in Detail

Negative Controls

A negative control is a group or sample that is not expected to respond to the treatment. It confirms that the experimental system is not producing false positives. In cell viability assays, for example, appropriate positive and negative controls are critical for reliable assessment. The selection of suitable controls was critical for reliable viability assessment in intervertebral disc organ cultures. Calcein AM/EthD-1 provided a straightforward approach but required protocol modifications, including Collagenase P pre-treatment, to ensure adequate tissue penetration. This method also requires immediate processing and imaging after harvesting.

In forensic science, the Phadebas Forensic Press Test is widely employed for presumptive saliva screening by observation of alpha-amylase activity. This enzyme hydrolyses starch embedded in the test paper, resulting in a blue reaction when positive. Although designed to assist in locating areas of possible saliva staining on exhibits, other alpha-amylase containing substances such as urine, vaginal secretions, faeces, and laundry detergents have also produced positive results, limiting the test's specificity. This example illustrates why negative controls matter: without testing known non-saliva substances, a laboratory might incorrectly conclude that a stain contains saliva.

Positive Controls

A positive control is a group or sample that is known to produce a response. It confirms that your experimental system is capable of detecting the effect you are studying. If your positive control does not respond, your assay may be faulty, and any negative result from the experimental group is uninterpretable.

In the intervertebral disc organ culture study, the researchers used appropriate positive and negative controls to evaluate the strengths and limitations of three staining methods. The LDH/EthD-1 method was unsuitable for short-term experiments because LDH signal persists for up to 36 hours after cell death, except under extreme conditions such as repeated snap-freezing. MTT/DAPI proved more suitable for short-term applications. These findings provide practical guidance for selecting and implementing viability assays, and the insights may also be applicable to other dense tissues such as tendon and cartilage.

Placebo and Sham Controls

A placebo control is a group that receives an inert treatment that looks and feels like the real treatment but has no active ingredients. A sham control is similar but involves a procedure that mimics the real procedure without the active component. These controls account for the psychological and physiological effects of receiving treatment.

In clinical trials of behavioral interventions, a well-designed control condition is an essential component to foster the unambiguous interpretation of study findings. Types of control conditions that have been used in fall prevention trials include no-treatment controls, usual-care controls, and attention controls. The attention control condition was designed to overcome limitations of previous trial designs by providing participants with the same amount of contact time and social interaction as the experimental group, but without the active behavioral components.

Active and Attention Controls

An active control is a group that receives an alternative treatment that is known to be effective. This is used when it would be unethical to give participants no treatment, such as when an effective treatment already exists. An attention control is a type of active control that matches the experimental group for the amount of attention, contact time, and social interaction, but does not provide the specific active components of the intervention.

The design of placebo or attention control conditions for community-based clinical trials of health behavior change interventions requires practical strategies. A well-designed control condition is an essential component of a clinical trial to foster the unambiguous interpretation of study findings. Pitfalls in the design of control conditions in clinical trials of behavioral interventions include failing to match for attention, failing to provide a credible alternative, and failing to monitor what control group members actually receive.

Historical and External Controls

A historical control is a group of subjects from a previous study or from a patient registry that is used as a comparison for the current study. There is increasing demand for utilization of external data, such as historical study data and patient registry data, to augment the control group in a randomized controlled trial. While such a study design could reduce the time and cost, how to maintain the study validity and integrity is one major statistical challenge that needs to be carefully addressed.

The quality process for using external data involves a two-step assessment of the similarity in patient characteristics between the current study and the external data source, and between the treatment and augmented control groups. This process is tailored to the confirmatory study using a two-stage design with an emphasis on the interaction process among stakeholders. If you use historical controls, you must document the similarity between the current and historical populations and discuss the limitations of this approach.

How to Choose the Right Control Group

Step 1: Define Your Research Question

Before you can choose a control group, you must know what question you are trying to answer. Are you asking whether a treatment works? Whether it works better than an existing treatment? Whether the effect is due to the active ingredient or to the act of receiving treatment? Each question requires a different type of control.

Step 2: Identify Potential Confounders

List all the factors other than your treatment that could affect your outcome. These include the act of being measured, the passage of time, natural recovery, and the attention of researchers. Your control group should account for as many of these confounders as possible.

Step 3: Consider Ethical Constraints

You cannot withhold treatment from a control group if withholding treatment would cause harm. In this case, you must use an active control or a usual-care control. You also cannot randomize subjects to groups if randomization would be unethical, such as when the intervention is known to be beneficial and the control would be denied that benefit.

Step 4: Assess Feasibility

Consider whether you can recruit enough subjects for both groups, whether you can maintain blinding, and whether you can prevent contamination. If randomization is not feasible, you must use a quasi-experimental design and acknowledge its limitations.

Step 5: Document Your Decision

Record the rationale for your choice of control group, the type of control used, and the procedures for maintaining the control condition. This documentation is essential for the interpretation of your results and for the replication of your study by other researchers.

Practical Implementation of Control Groups

Blinding and Allocation Concealment

Blinding means that the subjects, the researchers, or both do not know which group a subject is in. Single blinding typically means the subjects do not know their group assignment. Double blinding means both the subjects and the researchers do not know. Blinding reduces the risk of bias in the measurement of outcomes.

Allocation concealment means that the person assigning subjects to groups does not know which group the next subject will receive. This prevents selection bias. Both blinding and allocation concealment are important for maintaining the integrity of the control group.

Monitoring the Control Condition

You must monitor what the control group actually receives during the study. In behavioral intervention trials, control group members may seek the experimental intervention on their own, which is contamination. They may also drop out of the study at a higher rate than the experimental group, which is differential dropout. Both problems can be reduced by offering control group members an intervention after the study period or by providing them with a credible alternative intervention during the study.

Reporting Control Group Procedures

Control treatments in stroke motor rehabilitation trials are underdescribed relative to experimental treatments. Experimental groups had statistically more words in their procedures than did control groups, and experimental groups had statistically more references in their procedures than did control groups. Experimental groups also scored significantly higher on the total Template for Intervention Description and Replication checklist than did control groups. This poor reporting makes it difficult for other researchers to replicate the control condition and for readers to interpret the results.

When you write your methods section, describe the control group with the same level of detail as the experimental group. Include the specific procedures, the timing of measurements, the personnel involved, and any instructions given to control group members.

Records and Measurements for Control Groups

What to Record

For each control group, record the following information:

• The type of control used and the rationale for choosing it
• The number of subjects or samples in the control group
• The procedures for assigning subjects to the control group
• The specific interventions or procedures received by the control group
• The timing and methods of outcome measurement
• Any deviations from the protocol and the reasons for them
• The number of subjects who dropped out and the reasons for dropout
• Any evidence of contamination and how it was addressed

How to Measure Control Group Performance

You should measure the control group's outcomes with the same instruments and at the same time points as the experimental group. You should also measure process variables, such as attendance at sessions, adherence to instructions, and the credibility of the control condition. These process measures help you determine whether the control condition was implemented as intended.

Using the Experimental Design Assistant

The Experimental Design Assistant from the NC3Rs is a free online tool that helps researchers design experiments and identify potential sources of bias. It provides a visual representation of your experimental design and flags issues such as lack of blinding, inadequate randomization, and inappropriate control groups. Using such a tool before you begin your study can help you avoid common design errors.

Common Failure Patterns in Control Group Design

Failure to Match Attention and Contact Time

When the experimental group receives more attention from researchers than the control group, any difference in outcomes could be due to the attention instead of the treatment. This is a common failure in behavioral intervention trials. The solution is to provide the control group with an attention-matched intervention that does not contain the active components.

Failure to Prevent Contamination

Contamination occurs when control group members receive the experimental intervention. This can happen when control group members learn about the experimental intervention from other participants or from the media, or when researchers inadvertently provide experimental components to the control group. The solution is to monitor what the control group receives and to offer control group members an intervention after the study period.

Failure to Account for Differential Dropout

When control group members drop out at a higher rate than experimental group members, the remaining control group may no longer be comparable to the experimental group. This is a particular problem in exercise oncology trials, where control group members may become discouraged and leave the study. The solution is to offer control group members an intervention after the study period and to monitor dropout rates throughout the study.

Failure to Describe the Control Condition

When the control condition is underdescribed in the methods section, readers cannot determine what the control group actually received. This makes it difficult to interpret the results and to replicate the study. The solution is to describe the control condition with the same level of detail as the experimental condition.

Using an Inappropriate Historical Control

Historical controls can introduce bias if the current study population differs from the historical population in important ways. The solution is to assess the similarity in patient characteristics between the current study and the external data source, and between the treatment and augmented control groups. This assessment should be documented and reported.

Limitations of Control Groups

Threats to Internal Validity

Even with a well-designed control group, your study may be vulnerable to threats to internal validity. These include history effects, where events outside the study affect the outcome, maturation effects, where subjects change over time, and testing effects, where the act of measurement affects the outcome. The non-equivalent control group post-test-only design is particularly vulnerable to these threats because the groups are not randomly assigned.

Generalizability

The results of a study with a control group may not generalize to other populations, settings, or times. A control group that is well matched to the experimental group may still not be representative of the broader population. You should discuss the generalizability of your findings and the limitations of your control group.

Ethical Constraints

You cannot always use the ideal control group because of ethical constraints. For example, you cannot withhold an effective treatment from a control group, and you cannot randomize subjects to groups when randomization would be unethical. In these cases, you must use an alternative design and acknowledge its limitations.

Quality and Welfare Controls in Animal Research

The Three Rs

In animal research, the principles of replacement, reduction, and refinement guide the design of experiments. The Experimental Design Assistant from the NC3Rs supports these principles by helping researchers design experiments that use the minimum number of animals necessary to achieve their objectives and that minimize pain and distress. A well-designed control group is essential for reducing the number of animals needed because it increases the precision of the experiment.

Welfare Monitoring of Control Animals

Control animals must receive the same standard of care as experimental animals. They should be monitored for signs of pain, distress, and illness, and any welfare concerns should be addressed promptly. The control group should not be neglected in favor of the experimental group, as this would introduce a confounding variable and would be ethically unacceptable.

Professional Escalation Criteria

If you observe any of the following, you should escalate the issue to a supervisor, a veterinarian, or an institutional animal care and use committee:

• Control animals showing signs of pain or distress that are not being addressed
• Control group members receiving the experimental intervention, indicating contamination
• Control group dropout rates that are substantially higher than experimental group dropout rates
• Evidence that the control condition is not being implemented as intended
• Any deviation from the approved protocol that could affect the welfare of control animals

Safety and Regulatory Context

Good Research Practice

The Research Data Framework from the National Institute of Standards and Technology provides guidance on managing research data throughout the research lifecycle. Proper data management is essential for the integrity of your study, including the data from your control group. You should document your data collection procedures, store your data securely, and make your data available for verification and replication.

Reporting Guidelines

The EQUATOR Network provides reporting guidelines for health research, including guidelines for randomized controlled trials and observational studies. These guidelines specify what information should be reported about the control group, including the methods of assignment, the blinding procedures, and the description of the control condition. Following these guidelines improves the quality and transparency of your research.

Literature Resources

The NCBI Literature Resources and PubMed provide access to the biomedical literature, including studies on control group design. You can use these resources to identify previous studies in your field and to learn from their control group designs. The PubMed record for "The control group revisited" and the record for "An extension of control group design" provide historical perspectives on control group methodology.

Examples of Control Group Use in Life Sciences

Cell Viability Assays

In the intervertebral disc organ culture study, the researchers used appropriate positive and negative controls to evaluate the strengths and limitations of three staining methods for assessing cell viability. The selection of suitable controls was critical for reliable viability assessment. Calcein AM/EthD-1 provided a straightforward approach but required protocol modifications to ensure adequate tissue penetration. LDH/EthD-1 was unsuitable for short-term experiments because LDH signal persists for up to 36 hours after cell death. MTT/DAPI proved more suitable for short-term applications.

Nanoparticle Synthesis and Testing

In the study of zinc oxide nanoparticles synthesized using millet extracts, the researchers characterized the nanoparticles using multiple methods and evaluated their biological activity. The in vitro biological evaluation showed that millet-derived zinc oxide nanoparticles exhibited dose-dependent inhibition of carbohydrate-digesting enzymes. Cellular studies using INS-1 pancreatic beta-cells and 3T3-L1 adipocytes demonstrated enhanced glucose-stimulated insulin secretion and increased glucose uptake at non-cytotoxic concentrations. In this type of study, control groups would include cells treated with vehicle only and cells treated with a known positive control compound.

Disease Mechanism Studies

In the study of dopaminergic neurons in Parkinson's disease, the researchers used tyrosine hydroxylase reporter induced pluripotent stem cells generated by CRISPR/Cas9. They sorted neurons into pure TH-positive and TH-negative neurons upon differentiation into a dopaminergic neuron-containing cell culture. They characterized mitochondrial function in both dopaminergic and non-dopaminergic neurons from Parkinson's disease patients and controls. The use of neurons from healthy controls as a comparison group was essential for identifying differences in mitochondrial function and gene expression.

Cancer Research

In the study of miR-10b-3p in nasopharyngeal carcinoma, the researchers found that miR-10b-3p was reduced in EBV-positive nasopharyngeal carcinoma tissues and was further suppressed following EBV infection of non-malignant nasopharyngeal epithelial cells and EBV-negative nasopharyngeal carcinoma cell lines. Restoration of miR-10b-3p expression markedly inhibited cell proliferation, colony formation, migration, invasion, and epithelial-mesenchymal transition in EBV-positive cells, whereas inhibition of miR-10b-3p in EBV-negative cells produced the opposite effects. The use of EBV-negative cells as a control was essential for attributing the effects to EBV infection.

Leukemia Research

In the study of LDLRAD2 in acute myeloid leukemia, the researchers identified LDLRAD2 as a driver of extramedullary infiltration through analyses of patient samples, public datasets, AML cell models, and xenografts. LDLRAD2 increased glucose consumption, lactate production, endothelial tube formation, spleen infiltration, and microvascular density, whereas its knockdown reduced these phenotypes. The use of knockdown and overexpression models provided internal controls for the effects of LDLRAD2.

Frequently Asked Questions

What is the difference between a control group and an experimental group?

The experimental group receives the treatment or intervention being studied, while the control group does not receive the treatment. The control group is treated identically to the experimental group in every other way, so that any difference in outcomes can be attributed to the treatment. For example, in a drug trial, the experimental group receives the drug and the control group receives a placebo or no treatment.

What is a negative control in an experiment?

A negative control is a group or sample that is not expected to respond to the treatment. It confirms that the experimental system is not producing false positives. For example, in a cell viability assay, a negative control might be cells treated with culture medium only, without the drug being tested. If the negative control shows a response, the assay may be producing false positives.

What is a positive control in an experiment?

A positive control is a group or sample that is known to produce a response. It confirms that the experimental system is capable of detecting the effect you are studying. For example, in a cell viability assay, a positive control might be cells treated with a known cytotoxic agent. If the positive control does not respond, the assay may be faulty.

What is a placebo control?

A placebo control is a group that receives an inert treatment that looks and feels like the real treatment but has no active ingredients. It accounts for the psychological and physiological effects of receiving treatment. For example, in a clinical trial, the placebo group might receive a sugar pill that looks identical to the real drug.

When should I use an active control instead of a placebo control?

You should use an active control when it would be unethical to give participants no treatment, such as when an effective treatment already exists. An active control is a group that receives an alternative treatment that is known to be effective. This allows you to compare the experimental treatment with the standard of care.

What is contamination in a control group?

Contamination occurs when control group members receive the experimental intervention or when experimental group members do not receive it. This can happen when control group members learn about the experimental intervention from other participants or from the media, or when researchers inadvertently provide experimental components to the control group. Contamination can be reduced by offering control group members an intervention after the study period.

What is differential dropout in a control group?

Differential dropout occurs when control group members leave the study at a different rate than experimental group members. This can make the remaining control group no longer comparable to the experimental group. Differential dropout can be reduced by offering control group members an intervention after the study period and by monitoring dropout rates throughout the study.

Can I use historical data as a control group?

Yes, you can use historical data, such as from previous studies or patient registries, to augment or replace a concurrent control group. However, you must assess the similarity in patient characteristics between the current study and the external data source, and between the treatment and augmented control groups. Historical controls can introduce bias if the populations differ in important ways.

Related Articles

• Metagenomic Quality Control: Contamination, Host Reads, and Negative Controls
• Degrees, Certificates, and Scholarships in Bioinformatics and the Life Sciences: How to Choose (and Fund) Your Path
• Control Experimental
• Control Experimental
• Control Experimental

References and Further Reading

• Research Data Framework. National Institute of Standards and Technology.
• EQUATOR Network. EQUATOR Network.
• Experimental Design Assistant. NC3Rs.
• NCBI Literature Resources. National Center for Biotechnology Information.
• PubMed. National Library of Medicine.
• A review of the non-equivalent control group post-test-only design.. Nurse researcher, 2019.
• Experimental and quasi-experimental designs in implementation research.. Psychiatry research, 2020.
• The control group revisited.. Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology, 2023.
• Control group design, contamination and drop-out in exercise oncology trials: a systematic review.. PloS one, 2015.
• On the Reporting of Experimental and Control Therapies in Stroke Rehabilitation Trials: A Systematic Review.. Archives of physical medicine and rehabilitation, 2018.
• Design of control-group conditions in clinical trials of behavioral interventions.. Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing, 2007.
• An extension of control group design.. Psychological bulletin, 1949.
• A Study Design for Augmenting the Control Group in a Randomized Controlled Trial: A Quality Process for Interaction Among Stakeholders.. Therapeutic innovation & regulatory science, 2020.
• Verification of the Phadebas® forensic press test for the screening of human saliva.. 2026.
• Selective vulnerability of dopaminergic neurons in Parkinson's disease connects PRKN and differential expression of CHCHD2 and GPNMB.. 2026.
• Comparative green synthesis of zinc oxide nanoparticles using millet extracts and their physicochemical characterization and in vitro antidiabetic activity.. 2026.
• Optimization and comparison of different methods for assessing cell viability in intervertebral disc organ cultures.. 2026.
• Downregulation of miR-10b-3p by EBV promotes tumor growth and metastasis via ITGAV in nasopharyngeal carcinoma.. 2026.
• LDLRAD2 drives glycolysis and angiogenesis to promote extramedullary infiltration in acute myeloid leukemia.. 2026.
• The simultaneous replication design: The use of a multiple baseline to establish experimental control in single group social skills treatment studies. Journal of Behavior Therapy and Experimental Psychiatry, 1980.
• A CONTROL GROUP DESIGN FOR EXPERIMENTAL STUDIES OF DEVELOPMENTAL PROCESSES. Psychological Bulletin, 1968.

This article is educational and does not replace institutional policy, professional advice, or applicable safety and regulatory requirements.