Controlled Experiments: What They Are and Why They Matter

A controlled experiment is a study design where researchers actively manipulate one or more independent variables while holding other conditions constant, then compare outcomes against a control group that does not receive the manipulation. The purpose is to isolate cause and effect by ruling out alternative explanations for observed changes. This article explains the core components of controlled experiments, including control groups, variables, and randomization, and provides a practical framework for designing and evaluating them across research settings.

Controlled experiments are the foundation of causal inference in the life sciences, medicine, agriculture, and many applied fields. When you change one factor and observe a change in another, you need to know whether the observed change came from your intervention or from something else. A controlled experiment answers that question by creating a comparison that accounts for background variation. This framework matters for students designing thesis projects, researchers planning studies, life-science professionals interpreting published findings, and informed readers who want to evaluate claims about treatments, interventions, or management practices.

What Defines a Controlled Experiment

A controlled experiment has three defining features. First, the researcher assigns subjects or units to conditions. Second, at least one group receives no treatment or a placebo, forming the control. Third, the researcher attempts to keep all other conditions identical across groups so that any difference in outcome can be attributed to the treatment.

The essential logic is comparison. Without a control group, you cannot know whether a change would have occurred anyway. For example, if you give a supplement to a group of animals and their health improves, you cannot conclude the supplement caused the improvement unless you also track a comparable group that did not receive the supplement. The control group accounts for changes that happen naturally over time, from handling, from seasonal variation, or from the act of being observed.

Control Groups and Their Role

The control group serves as the baseline against which the treatment group is measured. In a randomized controlled trial, participants are assigned to either the active treatment or the control condition. The control may receive a placebo, a standard treatment, or no intervention, depending on the research question and ethical constraints.

A sham-controlled experiment provides a specific type of control. In a study of transcranial temporal interference stimulation for Parkinson's disease, twelve participants completed a randomized, double-blind, sham-controlled experiment in which each received either active stimulation or sham stimulation of the right globus pallidus internus. The sham condition ensured that any observed effects came from the stimulation itself instead of from the expectation of receiving treatment. The study reported that active stimulation reduced motor symptom scores by 6.64 points, or 14.7 percent, with particular improvement in bradykinesia and tremor. The sham control made this comparison meaningful because both groups experienced the same procedure, electrode placement, and study environment. See the PubMed record for the transcranial temporal interference stimulation study for the full methodology.

Independent and Dependent Variables

The independent variable is the factor that the researcher manipulates. The dependent variable is the outcome that is measured. In a controlled experiment, the researcher changes the independent variable and observes whether the dependent variable changes in response.

Consider a study of magnesium supplementation in adults with poor sleep quality. One hundred adults were randomly assigned to two groups matched by gender, age, and baseline sleep quality score. One group received a 320 mg magnesium supplement daily and the other received a placebo for seven weeks. The independent variable was the supplement versus placebo. The dependent variables included sleep quality scores, blood and urine biochemical measures, and inflammatory markers. The study found that sleep quality scores improved in both groups, from 10.4 to 6.6 on the Pittsburgh Sleep Quality Index, and that erythrocyte magnesium increased regardless of whether participants received magnesium or placebo. This result shows why the control group matters. Without the placebo group, the improvement in sleep quality might have been attributed to magnesium when it occurred in both groups. See the PubMed record for the magnesium supplementation study for details.

Extraneous Variables and Confounding

Extraneous variables are factors other than the independent variable that could influence the dependent variable. When an extraneous variable is systematically related to both the treatment and the outcome, it becomes a confounder. Confounders threaten the internal validity of an experiment because they offer alternative explanations for observed effects.

Randomization is the primary tool for managing confounders. By randomly assigning subjects to conditions, researchers distribute known and unknown confounding factors across groups. Randomization does not eliminate confounders, but it makes it unlikely that they are systematically concentrated in one group. In the magnesium study, participants were randomly assigned to groups matched by gender, age, and overall sleep quality score. This matching reduced the risk that baseline differences in these factors explained the results.

Randomization and Blinding

Randomization and blinding are procedural safeguards that protect the integrity of controlled experiments. They address different threats. Randomization addresses baseline imbalance. Blinding addresses bias that can arise after the experiment begins.

Why Randomization Matters

Randomization ensures that each subject has a known probability of being assigned to any group, and that assignment is not influenced by the researcher or the subject. This property supports the assumption that treatment groups are comparable at baseline. In public health research, natural or quasi experiments differ from randomized controlled experiments because exposure allocation is not controlled by researchers. These designs evaluate events or interventions that are difficult or impossible to manipulate experimentally, such as policy changes. Their causal claims rely heavily on the assumption that exposure allocation can be considered as-if randomized. See the BMC Medical Research Methodology article on natural and quasi experiments for a full discussion of this distinction.

Randomization also supports the validity of statistical tests. Most inferential statistics assume that observations are independent and that group assignment is random. When randomization is absent, the statistical model may not reflect the actual data-generating process, and p-values may be misleading.

Single-Blind and Double-Blind Designs

Blinding prevents knowledge of group assignment from influencing behavior or measurement. In a single-blind design, participants do not know which group they are in. In a double-blind design, neither participants nor researchers who interact with them know the assignment. Blinding is especially important when outcomes are subjective, such as pain ratings, sleep quality scores, or behavioral observations.

A randomized, double-blind, controlled trial of high-definition transcranial direct current stimulation for chronic insomnia assigned fifty-five patients to either active stimulation or sham stimulation for ten days. The double-blind design meant that neither the patients nor the assessors knew who received active treatment. Compared with the sham group, the active group showed decreases in Pittsburgh Sleep Quality Index scores, and exploratory polysomnography measures of sleep onset latency and sleep efficiency improved after treatment. The blinding protected the subjective sleep measures from expectation bias. See the PubMed record for the insomnia stimulation trial for the full protocol.

Blinding Efficacy and Its Assessment

Blinding is only useful if it works. Researchers should assess whether participants and assessors can guess group assignment at rates better than chance. In the transcranial temporal interference stimulation study, blinding efficacy was explicitly assessed alongside side effects. If participants can correctly guess their assignment, the blinding has failed, and the results may reflect expectation effects instead of treatment effects.

At a Glance: Core Components of a Controlled Experiment

The table below summarizes the essential components of a controlled experiment and the practical questions researchers should answer when designing one.

Types of Controlled Experiments

Controlled experiments take several forms depending on the setting, the research question, and the feasibility of randomization. Understanding the options helps researchers choose a design that matches their constraints.

Randomized Controlled Trials

The randomized controlled trial is the canonical controlled experiment. Participants are randomly assigned to treatment or control conditions, and outcomes are compared. This design is the standard for evaluating therapeutic interventions. Invasive deep brain stimulation has been shown to be effective for patients with advanced Parkinson's disease, but its use is limited to that population. Transcranial temporal interference stimulation was tested as a noninvasive alternative in a pilot study with twelve participants who had mild Parkinson's disease. The randomized, double-blind, sham-controlled design allowed the researchers to test feasibility and safety while controlling for placebo effects. See the PubMed record for the Parkinson's stimulation pilot study.

Sham-Controlled and Placebo-Controlled Designs

Sham and placebo controls are used when the treatment involves a procedure or substance that could produce effects through expectation alone. A sham control mimics the active treatment without delivering the active component. In the insomnia trial, the sham group received the same electrode placement and stimulation protocol but without active current delivery. This design distinguishes the biological effect of stimulation from the psychological effect of undergoing a procedure.

Natural and Quasi Experiments

Natural experiments evaluate events or interventions that researchers cannot manipulate. They combine features of experiments and non-experiments. Exposure allocation is not controlled by researchers, but the design evaluates the impact of events or processes that lead to differences in exposure. Natural experiments are, in theory, less susceptible to bias than other observational designs, but causal inference depends on the assumption that exposure allocation is as-if randomized. The target trial framework provides a systematic basis for evaluating this assumption. See the BMC Medical Research Methodology article on natural and quasi experiments for the full conceptual framework.

Laboratory and Field Experiments

Laboratory experiments offer high control over conditions but may lack external validity. Field experiments occur in real-world settings and offer greater generalizability but less control. The distinction between efficacy and effectiveness experiments is relevant here. Efficacy experiments provide evidence that a specific intervention works under ideal conditions. Effectiveness experiments show whether the intervention produces benefits under real-world conditions. In pediatric exercise science, laboratory-based efficacy studies led researchers and policy makers to consider physical activity in school settings, but large-scale school-based studies produced uneven results. The methodological and measurement issues that challenge laboratory methodologies in academic settings explain some of the discrepancy. See the PubMed record for the exercise and cognition review for this discussion.

Designing a Controlled Experiment

A well-designed controlled experiment follows a logical sequence from question to analysis. The steps below provide a practical workflow for planning a study that can support causal claims.

Step 1: Define the Research Question

Start with a specific question that names the population, the intervention, the comparison, and the outcome. A vague question produces a vague experiment. For example, instead of asking whether a feed additive improves health, ask whether adding a specific compound at a specific dose reduces the incidence of a defined health condition in a defined population over a defined period.

Step 2: Identify the Independent and Dependent Variables

State the independent variable in operational terms. What exactly will be manipulated, and how will the manipulation be verified? State the dependent variable in measurable terms. What instrument or procedure will be used, and what are its units? If the outcome is subjective, specify how it will be assessed and who will assess it.

Step 3: Define the Control Condition

Decide what the control group will receive. Options include no treatment, a placebo, a sham procedure, or a standard treatment. The choice depends on the research question and ethical considerations. The control condition should be as similar to the treatment condition as possible except for the active component.

Step 4: Plan Randomization

Generate the randomization sequence using a valid method, such as a random number generator or a randomization table. Decide whether to use simple randomization or restricted methods such as blocking or stratification. In the magnesium study, participants were randomly assigned to two groups matched by gender, age, and overall sleep quality score. This matching is a form of stratification that ensures balance on key prognostic factors.

Step 5: Implement Blinding

Identify everyone who could be influenced by knowing group assignment. Participants, caregivers, outcome assessors, and data analysts can all introduce bias. Implement blinding procedures that are appropriate to the study. Assess blinding efficacy where possible.

Step 6: Specify the Analysis Plan

Decide in advance how the data will be analyzed. What is the primary outcome? What statistical test will be used? How will missing data be handled? A pre-specified analysis plan reduces the risk of selective reporting and p-hacking.

Step 7: Document Everything

Maintain a complete record of the protocol, deviations, and data. The National Institute of Standards and Technology Research Data Framework addresses the infrastructure needed to manage research data across its lifecycle. Good documentation supports reproducibility and allows others to evaluate the validity of the experiment.

Options and Tradeoffs in Experimental Design

Every design choice involves tradeoffs between control, generalizability, cost, and feasibility. Researchers should make these tradeoffs explicit instead of implicit.

Sample Size and Statistical Power

Sample size determines the ability to detect a true effect. Small samples produce imprecise estimates and low power. The transcranial temporal interference stimulation study included twelve participants and was explicitly described as a pilot study requiring larger-scale confirmation. The authors noted that future studies were needed to confirm the benefits. Small pilot studies are useful for feasibility and safety, but they cannot support definitive conclusions.

Tradeoff Between Control and Realism

Laboratory experiments maximize control but may not reflect real-world conditions. Field experiments reflect real conditions but introduce uncontrolled variation. The exercise and cognition literature illustrates this tension. Laboratory-based efficacy studies suggested that physical activity benefits cognition, but school-based effectiveness studies produced uneven results. Researchers and practitioners should recognize that laboratory methodologies may not transfer directly to applied settings. See the PubMed record for the exercise and cognition review.

Tradeoff Between Randomization and Ethics

Randomization is not always ethical or feasible. When an intervention is known to be beneficial, denying it to a control group may be unethical. When an intervention is a policy or system change, researchers cannot randomly assign entire populations. Natural experiments offer an alternative in these situations, but they require strong assumptions about as-if randomization. See the BMC Medical Research Methodology article on natural and quasi experiments.

Tradeoff Between Blinding and Practicality

Blinding is not always possible. Surgical interventions, behavioral interventions, and dietary changes are difficult to blind. In these cases, researchers should use objective outcome measures where possible and assess the potential for bias. The fat processing techniques review compared different purification methods for adipose tissue and noted that centrifugation had a generally higher incidence of postoperative complications than other methods. The review also noted that more comparative studies were needed to draw conclusions about clinical efficacy and satisfaction. See the PubMed record for the fat processing techniques review.

Observations and Measurements in Controlled Experiments

The quality of a controlled experiment depends on the quality of its measurements. Poor measurement can obscure a true effect or create a false one.

Choosing Outcome Measures

Outcome measures should be valid, reliable, and responsive to change. Validity means the measure actually captures the construct of interest. Reliability means the measure produces consistent results under the same conditions. Responsiveness means the measure can detect changes that matter. In the insomnia trial, both polysomnography and self-reported sleep scales were used. Polysomnography provides objective measures of sleep onset latency and sleep efficiency, while self-reported scales capture the patient's experience. See the PubMed record for the insomnia stimulation trial.

Baseline Measurements

Baseline measurements describe the study population before the intervention begins. They serve two purposes. First, they allow researchers to check whether randomization produced comparable groups. Second, they allow for statistical adjustment if baseline imbalances exist. In the magnesium study, baseline assessment included body mass index, diet, blood and urine biochemical variables, and sleep quality. The groups were matched on gender, age, and overall sleep quality score at baseline. See the PubMed record for the magnesium supplementation study.

Repeated Measurements and Intra-Individual Variation

Some outcomes vary within individuals over time. Repeated measurements can reduce the impact of this variation. In the magnesium study, final assessments were made at five and seven weeks after supplement initiation, and the two time points were combined for statistical analysis to reduce intra-individual variation. This approach increases precision without requiring a larger sample.

Blinding of Outcome Assessment

When outcome assessment involves judgment, the assessor should be blinded to group assignment. In the transcranial temporal interference stimulation study, motor symptoms were assessed using the Movement Disorder Society-Unified Parkinson's Disease Rating Scale. The double-blind design protected this assessment from bias. See the PubMed record for the Parkinson's stimulation pilot study.

Records and Documentation

Complete records are essential for the integrity and reproducibility of controlled experiments. Documentation should cover the protocol, deviations, raw data, analysis code, and final results.

Protocol Documentation

The protocol describes the study design, population, interventions, outcomes, and analysis plan. A written protocol serves as the reference point for evaluating whether the study was conducted as planned. The EQUATOR Network provides reporting guidelines for health research that help authors document their methods transparently.

Data Management

Research data should be managed according to established frameworks. The National Institute of Standards and Technology Research Data Framework addresses the infrastructure needed to support research data across its lifecycle. Good data management includes version control, backup procedures, and clear naming conventions.

Deviations and Adverse Events

Any deviation from the protocol should be documented, including the date, the nature of the deviation, and the reason. Adverse events should be recorded and reported according to applicable requirements. In the transcranial temporal interference stimulation study, side effects were assessed and reported as mild and transient. See the PubMed record for the Parkinson's stimulation pilot study.

Reproducibility and Reporting

Transparent reporting allows others to evaluate and replicate the experiment. The EQUATOR Network provides a collection of reporting guidelines that cover different study types. Following these guidelines improves the completeness and transparency of research reports.

Common Failure Patterns in Controlled Experiments

Understanding how controlled experiments fail helps researchers avoid these failures and helps readers identify flawed studies.

Failure to Define the Control Condition Clearly

A control condition that differs from the treatment condition in multiple ways cannot isolate the active component. If the treatment group receives a supplement plus additional attention and the control group receives neither, any difference could come from the attention instead of the supplement. The control condition should differ from the treatment condition only in the active component.

Inadequate Randomization

Randomization can fail in several ways. The sequence may be predictable, allowing researchers to influence assignment. Allocation may not be concealed, allowing participants or researchers to know the next assignment. Or the randomization may not be implemented correctly, resulting in systematic differences between groups.

Failure to Blind When Blinding Is Possible

When outcomes are subjective and blinding is feasible, failing to blind introduces bias. Participants who know they received treatment may report better outcomes. Assessors who know the assignment may interpret ambiguous findings in favor of the treatment. The double-blind design used in the insomnia trial and the Parkinson's stimulation study protects against these biases. See the PubMed record for the insomnia stimulation trial and the PubMed record for the Parkinson's stimulation pilot study.

Confounding by Baseline Imbalance

Even with randomization, baseline imbalances can occur by chance. Researchers should compare baseline characteristics across groups and adjust for meaningful imbalances in the analysis. The magnesium study matched groups on gender, age, and sleep quality score to reduce this risk. See the PubMed record for the magnesium supplementation study.

Measurement Error and Misclassification

Poor measurement can obscure true effects or create false ones. Instruments should be calibrated, assessors should be trained, and procedures should be standardized. When measurement error is random, it reduces power. When it is systematic, it biases results.

Selective Reporting

Reporting only favorable outcomes or only significant results distorts the evidence base. Pre-specifying the analysis plan and reporting all outcomes, including null results, protects against this failure. The EQUATOR Network provides guidelines that support complete reporting.

Data Errors and Image Manipulation

Research misconduct and honest errors both threaten the integrity of controlled experiments. A corrigendum published for a study on Wnt signaling and pulmonary fibrosis documented multiple problems with the original figures, including immunohistochemical data that appeared in later publications, overlapping data panels, and identical protein bands across different experiments. The authors stated that they had inadvertently included some data incorrectly in the figures and provided revised versions. See the corrigendum for the Wnt signaling study. This case illustrates why readers should examine figures carefully and why journals should verify data integrity.

Limitations of Controlled Experiments

Controlled experiments have important limitations that researchers and readers should recognize.

Limited External Validity

Controlled experiments often use homogeneous populations and standardized conditions that do not reflect real-world diversity. Results may not generalize to other populations, settings, or time periods. The exercise and cognition literature shows that laboratory-based efficacy findings did not consistently translate to school settings. See the PubMed record for the exercise and cognition review.

Ethical Constraints

Randomization is not always ethical. When an intervention is known to be beneficial, withholding it from a control group is problematic. When an intervention is potentially harmful, exposing participants to it is problematic. These constraints limit the questions that controlled experiments can answer.

Cost and Feasibility

Controlled experiments can be expensive and time-consuming. Recruiting participants, delivering interventions, and collecting data require resources. The semantic scholar paper on supervised randomization addresses the cost of randomized experiments in direct marketing, where random targeting deviates from established targeting policy. Supervised randomization integrates existing scoring models into randomized trials to target relevant customers while maintaining consistent estimates of treatment effects through correction for active sample selection.

Artificial Conditions

The controlled setting itself can influence outcomes. Participants may behave differently when they know they are being studied. Procedures may not match real-world practice. These effects can limit the applicability of results.

Complexity of Real-World Interventions

Many real-world interventions involve multiple components that cannot be separated in a controlled experiment. Policy changes, system reforms, and complex behavioral interventions are difficult to randomize and difficult to standardize. Natural experiments offer an alternative, but they require strong assumptions. See the BMC Medical Research Methodology article on natural and quasi experiments.

Welfare and Safety Context

Controlled experiments involving human participants or animals must meet ethical and regulatory requirements. Researchers should consider welfare and safety throughout the design and conduct of the experiment.

Human Participant Protections

Research involving human participants should be reviewed by an institutional review board or ethics committee. Participants should provide informed consent. Risks should be minimized and balanced against potential benefits. In the transcranial temporal interference stimulation study, the intervention was described as a potential nonneurosurgical and safer alternative to invasive deep brain stimulation, and side effects were assessed and reported. See the PubMed record for the Parkinson's stimulation pilot study.

Animal Welfare

Research involving animals should follow the principles of replacement, reduction, and refinement. The NC3Rs Experimental Design Assistant is a web-based tool that helps researchers design rigorous animal experiments while considering welfare. The tool supports randomization, blinding, and sample size calculation.

Reporting Adverse Events

Adverse events should be documented and reported according to applicable requirements. In the insomnia trial, the researchers did not observe any effects on sleep stage ratio after intervention, and they noted that the therapy required replication and additional safety data. See the PubMed record for the insomnia stimulation trial.

Data Integrity and Research Misconduct

Research misconduct, including fabrication, falsification, and plagiarism, undermines the validity of controlled experiments. The corrigendum for the Wnt signaling study documented multiple data integrity problems and provided corrected figures. See the corrigendum for the Wnt signaling study. Researchers should maintain complete records, verify their data, and correct errors promptly.

Professional Escalation Criteria

Researchers and practitioners should know when to seek additional expertise or escalate concerns.

When to Consult a Statistician

Consult a statistician before finalizing the study design if the research question involves complex outcomes, multiple comparisons, or hierarchical data structures. A statistician can help with sample size calculation, randomization schemes, and analysis plans. The NC3Rs Experimental Design Assistant provides automated support for experimental design, but complex studies may require professional statistical input.

When to Consult an Ethics Committee

Consult an ethics committee before starting the study if the intervention involves more than minimal risk, if participants are vulnerable, or if the research raises ethical questions. Ethics review is required for most human participant research and for animal research in many jurisdictions.

When to Escalate Data Integrity Concerns

If you discover data errors, fabrication, or other integrity problems, escalate the concern to the appropriate institutional official. Journals have procedures for investigating and correcting the literature. The corrigendum for the Wnt signaling study demonstrates how journals handle identified problems. See the corrigendum for the Wnt signaling study.

When to Seek Replication

Treat single studies as preliminary evidence, especially when the sample is small or the effect is novel. The transcranial temporal interference stimulation study was explicitly described as a pilot study requiring larger-scale confirmation. See the PubMed record for the Parkinson's stimulation pilot study. Before changing practice based on a single study, look for replication and convergence across multiple studies.

Practical Assessment Steps for Evaluating a Controlled Experiment

Readers and reviewers can use the following steps to assess the quality of a controlled experiment.

Step 1: Identify the Research Question

What is the study trying to show? Is the question specific and answerable?

Step 2: Evaluate the Control Condition

What did the control group receive? Was the control condition appropriate for the research question? Did the control group differ from the treatment group only in the active component?

Step 3: Assess Randomization

How was assignment generated? Was allocation concealed? Were baseline characteristics balanced across groups?

Step 4: Check Blinding

Who was blinded? Was blinding assessed? Could bias have influenced the results?

Step 5: Evaluate the Measurements

Were the outcome measures valid and reliable? Were assessors trained and procedures standardized? Were baseline measurements collected?

Step 6: Examine the Analysis

Was the analysis plan pre-specified? Was the statistical test appropriate? Were missing data handled appropriately?

Step 7: Consider the Limitations

What are the threats to internal and external validity? Do the conclusions follow from the data? What additional evidence is needed?

Frequently Asked Questions

What is the difference between a control group and a control variable?

A control group is a set of subjects that does not receive the active treatment and serves as a baseline for comparison. A control variable is a factor that is held constant across all groups to prevent it from influencing the outcome. The control group addresses the question of what would have happened without the treatment. Control variables address the question of whether other factors could explain the results.

Why is randomization important in a controlled experiment?

Randomization ensures that each subject has a known probability of being assigned to any group and that assignment is not influenced by the researcher or the subject. This property makes it unlikely that baseline differences between groups are systematic. Randomization also supports the validity of statistical tests that assume independent observations and random assignment.

What is a sham control and when is it used?

A sham control mimics the active treatment without delivering the active component. It is used when the treatment involves a procedure that could produce effects through expectation alone. In the transcranial temporal interference stimulation study, the sham condition involved the same electrode placement and procedure without active stimulation. See the PubMed record for the Parkinson's stimulation pilot study.

How is blinding different from randomization?

Randomization determines which group a subject is assigned to. Blinding determines who knows that assignment. Randomization addresses baseline imbalance. Blinding addresses bias that can arise after the experiment begins, such as expectation effects or biased outcome assessment.

What is the difference between a controlled experiment and an observational study?

In a controlled experiment, the researcher manipulates the independent variable and assigns subjects to conditions. In an observational study, the researcher observes outcomes without manipulating exposure. Natural experiments occupy a middle ground. They evaluate events or interventions that researchers cannot manipulate, but they differ from other observational designs in that they evaluate the impact of events or processes that lead to differences in exposure. See the BMC Medical Research Methodology article on natural and quasi experiments.

What is the difference between efficacy and effectiveness experiments?

Efficacy experiments provide evidence that a specific intervention works under ideal conditions. Effectiveness experiments show whether the intervention produces benefits under real-world conditions. The exercise and cognition literature illustrates this distinction. Laboratory-based efficacy studies suggested that physical activity benefits cognition, but school-based effectiveness studies produced uneven results. See the PubMed record for the exercise and cognition review.

How large should the sample size be?

Sample size depends on the expected effect size, the variability of the outcome, the significance level, and the desired power. Small samples produce imprecise estimates and low power. Pilot studies with small samples can establish feasibility and safety but cannot support definitive conclusions. The transcranial temporal interference stimulation study included twelve participants and was described as a pilot study requiring larger-scale confirmation. See the PubMed record for the Parkinson's stimulation pilot study.

What should I do if I find errors in a published experiment?

If you find errors in a published experiment, contact the journal or the authors. Journals have procedures for investigating and correcting the literature. The corrigendum for the Wnt signaling study documented multiple data integrity problems and provided corrected figures. See the corrigendum for the Wnt signaling study. Prompt correction protects the integrity of the scientific record.

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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.
• Transcranial Temporal Interference Stimulation of the Right Globus Pallidus in Parkinson's Disease.. Movement disorders : official journal of the Movement Disorder Society, 2025.
• Diogenes Syndrome.. Frontiers of neurology and neuroscience, 2018.
• Magnesium supplementation improves indicators of low magnesium status and inflammatory stress in adults older than 51 years with poor quality sleep.. Magnesium research, 2010.
• Effects of high-definition transcranial direct current stimulation for the treatment of chronic insomnia: a randomized, double-blind, controlled trial.. Scientific reports, 2025.
• Topological dominance in peripheral vision.. Journal of vision, 2021.
• Fat Processing Techniques: A Narrative Review.. Aesthetic plastic surgery, 2021.
• Exercise and Cognition-2016.. Pediatric exercise science, 2017.
• Conceptualising natural and quasi experiments in public health.. BMC medical research methodology, 2021.
• [Corrigendum] Activated Wnt signaling induces myofibroblast differentiation of mesenchymal stem cells, contributing to pulmonary fibrosis.. 2026.
• Integrative Analysis of Triphala's Therapeutic Mechanisms in Periodontitis: Network Pharmacology-guided Investigation with Experimental Validation in Cellular and Animal Models.. 2026.
• Potentiation of the altered immune microenvironment following RF ablation of murine distant tumors with CTLA-4 immunotherapy.. 2026.
• Allicin inhibits kidney stone formation by suppressing the TNF/NF-κB pathway: Experimental validation through network pharmacology, in vitro, and in vivo studies.. 2026.
• TCEA1 Suppresses Acute Promyelocytic Leukemia by Upregulating C/EBPε and IRF8. 2026.
• Affordable Uplift: Supervised Randomization in Controlled Experiments. International Conference on Interaction Sciences, 2019.
• The design of controlled experiments in the evaluation of non-therapeutic interventions.. Journal of Chronic Diseases, 1982.
• NBER WORKING PAPER SERIES INSTRUMENTS OF DEVELOPMENT: RANDOMIZATION IN THE TROPICS, AND THE SEARCH FOR THE ELUSIVE KEYS TO ECONOMIC DEVELOPMENT. 2011.
• A Controlled Experiment on using Cognitive Work Analysis for System Engineering definition process. 2021 16th International System of Systems Engineering Conference Sose 2021, 2021.
• Interoperability experiment of VLAN tag swapped ethernet and transmitting high definition video through the layer-2 LSP between Japan and Belgium. IEICE Transactions on Communications, 2010.
• Continuous Experiment Definition Characteristics. Proceedings 46th Euromicro Conference on Software Engineering and Advanced Applications Seaa 2020, 2020.
• Scanning calorimeters controlled by an independent thermodynamic variable: Definitions and some metrological problems. Thermochimica Acta, 1985.
• An Online Controlled Experiment Design to Support the Transformation of Digital Learning towards Adaptive Learning Platforms. International Conference on Computer Supported Education Csedu Proceedings, 2022.

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