Avoiding Bias in Experiments: Common Pitfalls and How to Prevent Them

Bias in experiments is any systematic error that pushes results away from the true value in a consistent direction. Unlike random error, which averages out with more measurements, bias does not disappear with larger sample sizes and can lead to false conclusions even in well-powered studies. This article explains the main types of experimental bias, how to detect them in your own work, and practical strategies for prevention, with particular attention to the needs of students, researchers, and life-science professionals who design and interpret experiments.

What Counts as Bias in an Experiment

An experiment is biased when its design, conduct, or analysis produces results that systematically differ from the truth. The bias can come from how participants or samples are selected, how measurements are taken, how variables are controlled, or how data are interpreted. A biased experiment may still be precise, meaning it gives consistent results, but those results are consistently wrong in one direction.

The distinction between bias and random error matters for practical decisions. Random error affects the spread of your data and can be reduced by increasing sample size or improving measurement precision. Bias affects the center of your data and cannot be fixed by adding more observations. If your measurement instrument consistently reads high, measuring more samples will not correct that offset.

For researchers working with animals, plants, or human participants, bias can arise at every stage of the experimental pipeline. Understanding where bias enters is the first step toward preventing it. The Research Data Framework from the National Institute of Standards and Technology provides guidance on documenting data collection and analysis procedures, which helps researchers identify where bias may have entered their workflow.

At a Glance: Common Bias Types and Prevention Strategies

Selection Bias: When Your Sample Does Not Represent the Population

Selection bias occurs when the participants, animals, or samples in your experiment are not representative of the population you want to draw conclusions about. This can happen through conscious or unconscious choices about who or what gets included in the study.

Sources of Selection Bias

One common source is convenience sampling, where researchers use whatever subjects are easiest to obtain. A study on dairy cow behavior that uses only cows from one farm may not generalize to cows in other housing systems. Similarly, a clinical trial that recruits only young, healthy volunteers may not apply to older patients with multiple health conditions.

Another source is self-selection, where participants choose whether to be in the study. People who volunteer for nutrition studies may have different eating habits than the general population. In animal research, animals that are easier to handle may be more likely to be selected for behavioral studies, introducing a systematic difference.

Differential dropout is a third source. If certain types of subjects leave the study before completion, the final sample may differ from the initial sample. For example, if sicker animals are more likely to die or be removed from a feeding trial, the surviving animals may appear to have better outcomes than the treatment actually produces.

Preventing Selection Bias

Randomization is the primary tool for preventing selection bias in experiments. When subjects are randomly assigned to treatment groups, each group should be similar on average for both measured and unmeasured characteristics. The Campbell systematic reviews discussion paper on risk of bias emphasizes that equivalence of groups is one of the four common sources of bias across randomized controlled trials, quasi-experiments, and natural experiments.

For observational studies where randomization is not possible, researchers should carefully document inclusion and exclusion criteria and compare the characteristics of included and excluded subjects. The use of negative controls can help detect selection bias in observational research. As described in the Epidemiology paper on negative controls, negative controls are designed to detect both suspected and unsuspected sources of spurious causal inference. An exposure control uses a substance or condition that should have no causal effect on the outcome, while an outcome control uses an outcome that should not be affected by the exposure.

Practical Steps for Reducing Selection Bias

Define your target population clearly before starting the experiment. Write explicit inclusion and exclusion criteria and apply them consistently. Use a randomization procedure that cannot be influenced by the person enrolling subjects. In animal studies, this might mean using a computer-generated random sequence instead of alternating assignments. Document every subject that was screened, enrolled, and completed the study, along with reasons for exclusion or dropout.

Measurement Bias: When Your Instruments or Observers Distort Data

Measurement bias occurs when the way you measure your outcome systematically distorts the true value. This can happen because of faulty instruments, inconsistent procedures, or observer expectations.

Instrument Calibration and Technique

Instruments can drift over time or be improperly calibrated. A scale that reads 0.5 kg high will bias all weight measurements in the same direction. In laboratory work, pipettes that are not calibrated can introduce systematic errors in volume measurements. The Scientific Reports paper on bias in histopathology images demonstrates that even deep learning models can detect unintended patterns, such as the hospital of origin, instead of the biological features of interest. This finding shows that measurement tools, including computational ones, can carry hidden biases that affect results.

Measurement technique can also introduce bias. If one researcher measures animal weights in the morning and another measures them in the afternoon, the results may differ systematically. If the person measuring outcomes knows which treatment group each animal belongs to, they may unconsciously record measurements that confirm their expectations.

Observer Bias and Blinding

Observer bias is a well-documented phenomenon in animal behavior research. A PLoS One meta-analysis of nestmate recognition studies in ants found that only 29 percent of 79 studies were conducted blind. Studies that were not blind were more likely to report aggression among nestmates, and the effect size between nestmate and non-nestmate treatments was significantly larger in non-blind experiments. The authors concluded that confirmation bias, the tendency to interpret information in a way that confirms expectations, can distort study results and reduce reliability.

Blinding is the primary defense against observer bias. In a single-blind study, the person measuring the outcome does not know which treatment group each subject belongs to. In a double-blind study, neither the participants nor the researchers know the group assignments. Blinding is especially important when outcomes involve subjective judgments, such as scoring behavior, assessing tissue samples, or evaluating clinical improvement.

Reducing Measurement Bias

Calibrate instruments before and during the experiment using known standards. Document calibration dates and results. Standardize measurement procedures with written protocols that specify when, how, and by whom measurements are taken. Train all observers to use the same techniques and assess inter-observer reliability. Use blinding whenever possible, and if blinding is not feasible, document why and consider using automated measurement methods.

Confounding: When an Unmeasured Variable Distorts the Relationship

Confounding occurs when a third variable is associated with both the exposure and the outcome, creating a spurious relationship or masking a real one. In experiments, randomization helps prevent confounding by balancing both measured and unmeasured variables across treatment groups. In observational studies, confounding is a constant threat.

How Confounding Works

Consider a study examining whether a new feed additive improves weight gain in pigs. If the pigs receiving the additive are also housed in a different barn with better ventilation, the housing difference, not the additive, may explain the weight gain difference. The barn is a confounder because it is associated with both the treatment and the outcome.

Confounding can also arise from time-related factors. If all animals in the treatment group are tested in the morning and all control animals are tested in the afternoon, time of day becomes a confounder. Any diurnal variation in the outcome measure will be attributed to the treatment.

Detecting Confounding

The Epidemiology paper on negative controls argues that negative controls should be more commonly employed in observational studies to detect confounding and other sources of error. A negative control exposure is something that should not affect the outcome through the causal pathway of interest. If the negative control shows an association with the outcome, confounding or bias is present.

Researchers can also detect confounding by comparing the characteristics of treatment groups at baseline. If groups differ on important variables despite randomization, those variables may confound the results. Stratified analysis or statistical adjustment can help address measured confounders, but unmeasured confounders remain a threat.

Controlling Confounding

Randomization is the most effective method for controlling confounding in experiments. When randomization is not possible, matching can be used to ensure that treatment groups are similar on key variables. Restriction, where the study is limited to subjects with specific characteristics, can also reduce confounding but limits generalizability.

Statistical methods such as stratification, multivariable regression, and propensity score analysis can adjust for measured confounders. However, these methods cannot control for unmeasured confounders. The Campbell systematic reviews framework provides signaling questions to help reviewers assess whether confounding has been adequately addressed in different study designs.

Design-Related Bias: Problems in How the Experiment Is Structured

Some biases arise from the fundamental structure of the experiment instead of from selection, measurement, or confounding. These design-related biases can be subtle and difficult to detect without careful attention to experimental architecture.

Carryover and Order Effects

In crossover designs where each subject receives multiple treatments, the effects of one treatment may carry over into the next period. This is a particular concern in feeding trials where the previous diet may continue to influence metabolism after the diet is changed. Adequate washout periods between treatments can reduce carryover effects, but determining the appropriate washout duration requires knowledge of the biological persistence of the treatment effect.

Order effects occur when the sequence of treatments influences outcomes. If all subjects receive treatment A before treatment B, any time-related trend will be confounded with treatment order. Randomizing the order of treatments across subjects can help address this problem.

Serial Dependence in Repeated Measures

When measurements are taken repeatedly from the same subject, responses may be influenced by previous measurements. Research on visual working memory published in the British Journal of Psychology identified two types of serial dependence operating across and within trials. Current stimuli were attracted toward preceding stimuli that had been cued for report, while within the same trial, the second stimulus was repulsed from the first. These findings demonstrate that responses to current stimuli can be systematically influenced by past stimuli, a form of bias that can affect experiments using repeated measures designs.

Inappropriate Averaging

Averaging data inappropriately can introduce bias. The Journal of Parapsychology paper on bias caused by inappropriate averaging in experiments with randomized stimuli highlights this problem. When data are averaged across conditions that should be analyzed separately, or when individual differences are ignored, the resulting averages may not represent any actual subject or condition.

Designing to Minimize Structural Bias

Use designs that match your research question. For repeated measures, consider the potential for carryover and order effects and plan appropriate washout periods and randomization. Analyze data at the appropriate level, accounting for repeated measures and clustering. The NC3Rs Experimental Design Assistant provides an online tool that helps researchers design experiments with appropriate randomization, blinding, and sample size calculations.

Analysis and Interpretation Bias: Problems After Data Collection

Bias can enter the experiment after data collection through inappropriate analysis or interpretation. These biases are particularly dangerous because they can be difficult to detect in the final report.

Confirmation Bias in Analysis

Confirmation bias, the tendency to interpret information in a way that confirms expectations, can affect data analysis and interpretation. The PLoS One study on nestmate recognition found that studies conducted without blinding were more likely to report results consistent with the researchers expectations. This finding has implications beyond animal behavior research. Any researcher who analyzes data with strong prior expectations may unconsciously make analytic choices that favor those expectations.

Multiple Comparisons and Selective Reporting

When researchers test many hypotheses and report only the significant results, the published literature can become biased. This is sometimes called publication bias or selective reporting. The EQUATOR Network provides reporting guidelines that help researchers document their methods and results transparently, reducing the opportunity for selective reporting.

P-Hacking and Data Dredging

P-hacking refers to analyzing data in multiple ways until a significant result is found. This can involve adding or removing outliers, trying different statistical models, or splitting and combining groups until a desired p-value is achieved. P-hacking inflates the false positive rate and produces results that do not replicate.

Preventing Analysis Bias

Pre-specify your analysis plan before collecting data. Decide which primary outcome will be tested, which statistical model will be used, and how missing data will be handled. Document any deviations from the analysis plan and explain why they were made. Use blinding during analysis when possible, so that the person analyzing the data does not know which group is which. Consider registering your study protocol in a public registry to demonstrate that your analysis plan was established before data collection.

Expectation Bias and the Role of Blinding

Expectation bias occurs when researchers or participants expectations influence the outcome of the experiment. This can happen through conscious or unconscious processes and is a particular concern in studies with subjective outcomes.

How Expectations Influence Results

Researchers who expect a treatment to work may unconsciously treat subjects differently, record measurements differently, or interpret ambiguous results in favor of their hypothesis. Participants who expect a treatment to work may report improvements that are not objectively measurable. The Journal of Parapsychology paper on removing expectation bias recommends strategies for eliminating the influence of expectation bias in experiments, emphasizing the importance of blinding and standardized procedures.

Blinding as a Solution

Blinding prevents expectations from influencing results by keeping treatment assignments hidden. In single-blind studies, the participants do not know their treatment assignment. In double-blind studies, neither participants nor researchers know the assignments. In triple-blind studies, the data analysts are also blinded.

Blinding is not always possible. In some animal studies, the treatment may produce visible effects that reveal group assignment. In surgical studies, it may be impossible to blind the surgeon. In these cases, researchers should use objective outcome measures whenever possible and document the limitations of blinding.

Implementing Blinding Effectively

Use coded labels for treatment groups so that the people measuring outcomes do not know which code corresponds to which treatment. Keep the code in a secure location and do not reveal it until data collection is complete. If blinding is broken during the study, document when and why it was broken and consider whether the data collected after the break should be excluded from analysis.

Negative Controls: A Tool for Detecting Bias

Negative controls are experimental conditions that should produce a null result if the experiment is working correctly. They are a routine precaution in biologic laboratory experiments and can be adapted for use in other types of research.

Types of Negative Controls

The Epidemiology paper on negative controls distinguishes two types of negative controls. Exposure controls use an exposure that should not affect the outcome through the causal pathway of interest. Outcome controls use an outcome that should not be affected by the exposure. If a negative control shows an unexpected association, bias or confounding is present.

Examples of Negative Controls

In a study of a new vaccine, a negative control exposure might be a saline injection that should not protect against disease. If the saline group shows lower disease rates than expected, the experiment may have a problem with contamination or measurement error. In an observational study of air pollution and respiratory disease, a negative control outcome might be a condition that should not be affected by air pollution, such as bone fractures. If air pollution is associated with bone fractures, the study may have unmeasured confounding.

Using Negative Controls in Your Experiments

Include negative controls in your experimental design whenever possible. Decide in advance what result you expect from the negative control and what action you will take if the negative control produces an unexpected result. Document negative control results in your lab notebook or study records so that they can be reviewed if questions arise about the validity of the experiment.

Practical Workflow for Identifying and Mitigating Bias

The following workflow can help researchers identify and mitigate bias at each stage of the experimental process.

Step 1: Define the Research Question and Target Population

Write a clear research question that specifies the population, exposure, outcome, and time frame. Identify the population to which you want to generalize your results. Consider whether your sampling method will produce a representative sample.

Step 2: Design the Experiment to Minimize Bias

Use randomization to assign subjects to treatment groups. Use blinding to prevent expectations from influencing results. Include negative controls to detect bias. Choose outcome measures that are objective and reliable. Plan for adequate sample sizes to detect meaningful effects.

Step 3: Document Your Methods Before Starting

Write a detailed protocol that specifies inclusion and exclusion criteria, randomization procedures, blinding methods, measurement protocols, and analysis plans. The Research Data Framework from NIST provides guidance on documenting research data and methods. Consider using the NC3Rs Experimental Design Assistant to help plan your experiment.

Step 4: Monitor Data Collection for Bias

Check instruments regularly for calibration drift. Monitor for differential dropout and document reasons for exclusion. Watch for unintended differences between treatment groups. If problems are detected, address them immediately and document what was done.

Step 5: Analyze Data According to Your Pre-Specified Plan

Follow your analysis plan. If you deviate from the plan, document the deviation and explain why. Check for confounding by comparing baseline characteristics of treatment groups. Use appropriate statistical methods that account for the design of your experiment.

Step 6: Interpret Results With Appropriate Caution

Consider alternative explanations for your findings. Discuss the limitations of your study, including any sources of bias that could not be fully controlled. Report your methods and results transparently so that others can assess the risk of bias in your study.

Records and Measurements for Bias Detection

Maintaining detailed records is essential for detecting and documenting bias. The following records can help researchers identify problems and demonstrate that appropriate precautions were taken.

Randomization Records

Document the randomization procedure, including the random sequence and how it was generated. Record the date and time of randomization and the person who performed it. Keep the randomization code in a secure location and document who has access to it.

Blinding Records

Document who was blinded and who was not. Record when blinding was broken and why. If blinding was broken for one subject, note whether the data from that subject were affected.

Calibration Records

Record calibration dates and results for all measurement instruments. Note any adjustments that were made. If an instrument was found to be out of calibration, document the period during which it may have been inaccurate and consider whether data collected during that period should be excluded.

Subject Tracking Records

Maintain a log of all subjects screened, enrolled, and completed. Record reasons for exclusion and dropout. Compare the characteristics of subjects who completed the study with those who did not to assess the potential for attrition bias.

Protocol Deviations

Document any deviations from the study protocol, including when they occurred, why they occurred, and what was done. Protocol deviations can introduce bias if they affect some treatment groups more than others.

Common Failure Patterns in Bias Prevention

Even experienced researchers can fall into predictable patterns of bias. Recognizing these patterns can help you avoid them.

Failure to Blind When Blinding Is Possible

Researchers sometimes skip blinding because it is inconvenient or because they believe their measurements are objective. The PLoS One meta-analysis found that only 29 percent of nestmate recognition studies were conducted blind, despite the fact that the behavioral assays involved subtle and subjective judgments. If your outcome involves any subjective judgment, blinding is likely necessary.

Inadequate Randomization Procedures

Some randomization procedures are not truly random. Alternating assignments, randomizing by day of the week, or using a haphazard method can introduce bias if the person enrolling subjects can predict or influence the assignment. Use a computer-generated random sequence or another method that cannot be influenced by the person enrolling subjects.

Ignoring Baseline Imbalances

Even with randomization, treatment groups may differ on some baseline characteristics by chance. Researchers sometimes ignore these imbalances or fail to check for them. Compare baseline characteristics of treatment groups and consider adjusting for important imbalances in the analysis.

Confusing Correlation With Causation

Observational studies can identify associations but cannot establish causation without careful attention to confounding. The Epidemiology paper on negative controls emphasizes that noncausal associations between exposures and outcomes are a threat to the validity of causal inference in observational studies. Use negative controls and other tools to detect and address confounding.

Overinterpreting Subgroup Analyses

When researchers analyze many subgroups, some will show significant results by chance. Subgroup analyses should be pre-specified and interpreted with caution. If you did not plan a subgroup analysis in advance, treat its results as exploratory instead of confirmatory.

Limitations of Bias Prevention Strategies

Bias prevention strategies have limitations that researchers should understand.

Randomization Does Not Guarantee Balance

Randomization balances groups on average, but by chance, groups may still differ on important variables. This is more likely with small sample sizes. Check baseline characteristics of treatment groups even when randomization was used.

Blinding Is Not Always Possible

Some interventions cannot be blinded. Surgical procedures, behavioral interventions, and dietary interventions may be impossible to blind. In these cases, use objective outcome measures and consider using blinded outcome assessors who were not involved in delivering the intervention.

Negative Controls Cannot Detect All Bias

Negative controls are useful for detecting certain types of bias and confounding, but they cannot detect all sources of error. The Epidemiology paper on negative controls notes that additional work is needed to specify the conditions under which negative controls will be sensitive detectors of other sources of error in observational studies.

Statistical Adjustment Has Limits

Statistical methods can adjust for measured confounders but cannot control for unmeasured confounders. Residual confounding may remain even after adjustment. The Campbell systematic reviews framework provides signaling questions to help assess whether confounding has been adequately addressed.

Welfare and Safety Context for Animal Experiments

For researchers working with animals, bias prevention has direct implications for animal welfare and research ethics.

Bias and Animal Welfare

Biased experiments can lead to incorrect conclusions about the safety or efficacy of treatments. If a biased experiment suggests that a treatment is effective when it is not, animals may be exposed to ineffective treatments. If a biased experiment suggests that a treatment is safe when it is not, animals may be harmed. Preventing bias is therefore an ethical obligation, beyond a scientific one.

The Three Rs and Experimental Design

The principles of Replacement, Reduction, and Refinement guide ethical animal research. Good experimental design, including bias prevention, supports all three Rs. Reducing bias means that fewer animals are needed to obtain reliable results, supporting the goal of Reduction. The NC3Rs Experimental Design Assistant is designed to help researchers improve experimental design and reduce the number of animals needed.

Reporting Guidelines for Animal Research

The EQUATOR Network provides reporting guidelines for many types of research, including animal studies. Following reporting guidelines helps ensure that methods are described in enough detail for others to assess the risk of bias. Transparent reporting also supports the reproducibility of research.

Professional Escalation Criteria

Some situations require escalation to a supervisor, institutional review board, or other authority. The following criteria indicate when professional escalation may be appropriate.

Unexpected Negative Control Results

If a negative control produces an unexpected result, this may indicate a serious problem with the experiment. Stop data collection and consult with a supervisor or statistician before continuing.

Evidence of Data Fabrication or Falsification

If you suspect that data have been fabricated or falsified, report your concerns to the appropriate institutional authority. Do not attempt to investigate on your own.

Serious Protocol Violations

If a protocol violation affects the validity of the experiment, document the violation and consult with a supervisor. In some cases, the experiment may need to be restarted or the affected data excluded.

Unblinding That Compromises the Study

If blinding is broken in a way that compromises the validity of the study, consult with a supervisor or statistician. The Journal of Parapsychology paper on expectation bias recommends strategies for removing the influence of expectation bias, but some situations require professional judgment about whether the study can continue.

Confounding That Cannot Be Addressed

If you identify a confounder that cannot be addressed through design or analysis, consult with a statistician. In some cases, the study may need to be redesigned or the research question may need to be revised.

Frequently Asked Questions

What is the difference between bias and random error in experiments?

Bias is a systematic error that pushes results in one direction, while random error causes results to vary around the true value. Bias does not decrease with larger sample sizes, but random error does. A biased experiment can be precise, meaning it gives consistent results, but those results are consistently wrong in one direction.

How can I tell if my experiment has selection bias?

Check whether your sample represents the population you want to draw conclusions about. Compare the characteristics of subjects who were screened, enrolled, and completed the study. If certain types of subjects were more likely to be excluded or to drop out, selection bias may be present. Document your inclusion and exclusion criteria and apply them consistently.

Why is blinding important in experiments?

Blinding prevents expectations from influencing results. Researchers who know which treatment group a subject belongs to may unconsciously measure outcomes differently or interpret ambiguous results in favor of their expectations. The PLoS One meta-analysis of nestmate recognition studies found that non-blind studies were more likely to report results consistent with researchers expectations.

What is a negative control and how do I use one?

A negative control is a condition that should produce a null result if the experiment is working correctly. Exposure controls use an exposure that should not affect the outcome, while outcome controls use an outcome that should not be affected by the exposure. If a negative control shows an unexpected association, bias or confounding is present. The Epidemiology paper on negative controls provides examples and guidance.

How do I choose between randomization and matching?

Randomization is the preferred method for controlling confounding in experiments because it balances both measured and unmeasured variables across treatment groups. Matching can be used when randomization is not possible, but it only controls for the variables that are matched. The Campbell systematic reviews framework emphasizes that equivalence of groups is a key source of bias across study designs.

What should I do if I find a confounder in my data?

First, determine whether the confounder was measured. If it was, you can adjust for it in the analysis using stratification or multivariable regression. If it was not measured, you may need to acknowledge the limitation and consider whether the study can answer the research question. Consult with a statistician if you are unsure how to proceed.

How can I prevent confirmation bias in my research?

Use blinding to prevent expectations from influencing data collection and analysis. Pre-specify your analysis plan before collecting data. Document any deviations from the plan and explain why they were made. Consider having a colleague who was not involved in the study review your methods and results.

What are the most common mistakes researchers make in bias prevention?

Common mistakes include skipping blinding when it is possible, using inadequate randomization procedures, ignoring baseline imbalances, confusing correlation with causation, and overinterpreting subgroup analyses. The Campbell systematic reviews framework identifies four common sources of bias across study designs: equivalence of groups, fidelity of study conditions, adequacy of measurement, and reporting of analyses.

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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.
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• Negative controls: a tool for detecting confounding and bias in observational studies.. Epidemiology (Cambridge, Mass.), 2010.
• Intergroup bias in punishing behaviors of adults with autism spectrum disorder.. Frontiers in psychiatry, 2022.
• Bias reduction in representation of histopathology images using deep feature selection.. Scientific reports, 2022.
• Confirmation bias in studies of nestmate recognition: a cautionary note for research into the behaviour of animals.. PloS one, 2013.
• Risk of Bias in Experiments, Quasi-Experiments and Natural Experiments Across Disciplines: Discussion Paper and Assessment Framework.. Campbell systematic reviews, 2026.
• Two types of serial dependence in visual working memory.. British journal of psychology (London, England : 1953), 2019.
• Scientific novelty beyond the experiment.. Microbial biotechnology, 2023.
• The Subthalamic Nucleus Controls Action Timing under Threat.. 2026.
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• Types of DOI errors of cited references in Web of Science with a cleaning method. Scientometrics, 2019.
• On the impact of different types of errors on trust in human-robot interaction. 2019.
• Applying Linear Forms of Pseudo-Second-Order Kinetic Model for Feasibly Identifying Errors in the Initial Periods of Time-Dependent Adsorption Datasets. Water, 2023.
• Numerical analysis and experimental investigation into the effects of manufacturing errors on the running accuracy of the aerostatic porous spindle. 2018.
• Towards a typology of experimental errors: An epistemological view. 1989.
• Experimental errors and their effect on analyzing circular dichroism spectra of proteins.. Analytical Biochemistry, 1982.
• On the Tagged Ratio Methods to Remove the Bias of Estimate Caused by Type C Systematic Errors in the Petersen-Type Tagging Experiment. Nippon Suisan Gakkaishi, 1962.
• A bias caused by inappropriate averaging in experiments with randomized stimuli. Journal of Parapsychology, 2006.
• How to remove the influence of expectation bias in presentiment and similar experiments: A recommended strategy. Journal of Parapsychology, 2014.

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