Common Biases in Scientific Research

These are prompts to examine mechanisms, not evidence of misconduct. Connect a concern to the particular result and its likely consequences; use the matching formal tool when needed. Sources and current tool scopes are in review_sources.md.

Cognitive Biases Affecting Researchers

1. Confirmation Bias

Description: Tendency to search for, interpret, and recall information that confirms preexisting beliefs.

Manifestations:

• Designing studies that can only support the hypothesis
• Interpreting ambiguous results as supportive
• Remembering hits and forgetting misses
• Selectively citing literature that agrees

Mitigation:

• Preregister hypotheses and analysis plans
• Actively seek disconfirming evidence
• Use blinded data analysis
• Consider alternative hypotheses

2. Hindsight Bias (I-Knew-It-All-Along Effect)

Description: After an event, people perceive it as having been more predictable than it actually was.

Manifestations:

• HARKing (Hypothesizing After Results are Known)
• Claiming predictions that weren't made
• Underestimating surprise at results

Mitigation:

• Document predictions before data collection
• Preregister studies
• Distinguish exploratory from confirmatory analyses

3. Publication Bias (File Drawer Problem)

Description: Positive/significant results are more likely to be published than negative/null results.

Manifestations:

• Literature appears to support effects that don't exist
• Overestimation of effect sizes
• Inability to estimate true effects from published literature

Mitigation:

• Publish null results
• Use preregistration and registered reports
• Conduct systematic reviews with grey literature
• When appropriate, examine funnel asymmetry as a small-study-effects diagnostic; asymmetry does not uniquely diagnose publication bias

4. Anchoring Bias

Description: Over-reliance on the first piece of information encountered.

Manifestations:

• Initial hypotheses unduly influence interpretation
• First studies in a field set expectations
• Pilot data biases main study interpretation

Mitigation:

• Consider multiple initial hypotheses
• Evaluate evidence independently
• Use structured decision-making

5. Availability Heuristic

Description: Overestimating likelihood of events based on how easily examples come to mind.

Manifestations:

• Overemphasizing recent or dramatic findings
• Neglecting base rates
• Anecdotal evidence overshadowing statistics

Mitigation:

• Consult systematic reviews, not memorable papers
• Consider base rates explicitly
• Use statistical thinking, not intuition

6. Bandwagon Effect

Description: Adopting beliefs because many others hold them.

Manifestations:

• Following research trends without critical evaluation
• Citing widely-cited papers without reading
• Accepting "textbook knowledge" uncritically

Mitigation:

• Evaluate evidence independently
• Read original sources
• Question assumptions

7. Belief Perseverance

Description: Maintaining beliefs even after evidence disproving them.

Manifestations:

• Defending theories despite contradictory evidence
• Finding ad hoc explanations for discrepant results
• Dismissing replication failures

Mitigation:

• Explicitly consider what evidence would change your mind
• Update beliefs based on evidence
• Distinguish between theories and ego

8. Outcome Bias

Description: Judging decisions based on outcomes rather than the quality of the decision at the time.

Manifestations:

• Valuing lucky guesses over sound methodology
• Dismissing good studies with null results
• Rewarding sensational findings over rigorous methods

Mitigation:

• Evaluate methodology independently of results
• Value rigor and transparency
• Recognize role of chance

Experimental and Methodological Biases

9. Selection Bias

Description: Selection can impair representativeness or create biased exposure-outcome associations by conditioning on participation, survival, or another common effect. These are related but distinct problems; a nonrepresentative sample is not automatically biased for every within-sample contrast.

Types:

• Sampling bias: Non-random sample
• Attrition bias: Systematic dropout
• Volunteer bias: Self-selected participants differ
• Berkson's bias: Conditioning on hospitalization influenced by both exposure and disease can create an association (collider bias)
• Survivorship bias: Only examining "survivors"

Detection:

• Compare characteristics of participants vs. target population
• Analyze dropout patterns
• Consider who is missing from the sample

Mitigation:

• Random sampling
• Track and analyze non-responders
• Use strategies to minimize dropout
• Report participant flow diagrams

10. Observer Bias (Detection Bias)

Description: Researchers' expectations influence observations or measurements.

Manifestations:

• Measuring outcomes differently across groups
• Interpreting ambiguous results based on group assignment
• Unconsciously cueing participants

Mitigation:

• Blinding of observers/assessors
• Objective, automated measurements
• Standardized protocols
• Inter-rater reliability checks

11. Performance Bias

Description: Systematic differences in care provided to comparison groups.

Manifestations:

• Treating experimental group differently
• Providing additional attention to one group
• Differential adherence to protocols

Mitigation:

• Standardize all procedures
• Blind participants and providers
• Use placebo controls
• Monitor protocol adherence

12. Measurement Bias (Information Bias)

Description: Systematic errors in how variables are measured.

Types:

• Recall bias: Systematic differences in accuracy of recall
• Social desirability bias: Responding in socially acceptable ways
• Interviewer bias: Interviewer's characteristics affect responses
• Instrument bias: Measurement tools systematically err

Mitigation:

• Use validated, objective measures
• Standardize data collection
• Blind participants to hypotheses
• Verify self-reports with objective data

13. Confounding Bias

Description: Effect of extraneous variable mixed with the variable of interest.

Examples:

• Age confounding relationship between exercise and health
• Socioeconomic status confounding education and outcomes
• Indication bias in treatment studies

Mitigation:

• Randomization
• Matching
• Statistical adjustment
• Stratification
• Restriction

Choose adjustment using the causal question and temporal structure. Adjusting every measured variable can induce collider bias or remove part of the effect through a mediator; matching and regression do not eliminate unmeasured confounding by themselves.

14. Reporting Bias

Description: Selective reporting of results.

Types:

• Outcome reporting bias: Selectively reporting outcomes
• Time-lag bias: Delayed publication of negative results
• Language bias: Publishing positive results in English
• Citation bias: Preferentially citing positive studies

Mitigation:

• Preregister all outcomes
• Report all planned analyses
• Distinguish primary from secondary outcomes
• Use study registries

15. Spectrum Bias

Description: Test performance varies depending on the spectrum of disease severity in the sample.

Manifestations:

• Diagnostic tests appearing more accurate in extreme cases
• Treatment effects differing by severity

Mitigation:

• Test in representative samples
• Report performance across disease spectrum
• Avoid unrepresentative two-gate diagnostic samples of extreme cases and healthy controls when the target is accuracy among patients presenting for testing

16. Lead-Time Bias

Description: Apparent survival benefit due to earlier detection, not improved outcomes.

Example:

• Screening detecting disease earlier makes survival seem longer, even if death occurs at same age

Mitigation:

• Measure mortality, not just survival from diagnosis
• Use randomized screening trials
• Consider length-time and overdiagnosis bias

17. Length-Time Bias

Description: Screening disproportionately detects slower-growing, less aggressive cases.

Example:

• Slow-growing cancers detected more often than fast-growing ones, making screening appear beneficial

Mitigation:

• Randomized trials with mortality endpoints
• Consider disease natural history

18. Response Bias

Description: Systematic pattern in how participants respond.

Types:

• Acquiescence bias: Tendency to agree
• Extreme responding: Always choosing extreme options
• Neutral responding: Avoiding extreme responses
• Demand characteristics: Responding based on perceived expectations

Mitigation:

• Mix positive and negative items
• Use multiple response formats
• Blind participants to hypotheses
• Use behavioral measures

Statistical and Analysis Biases

19. P-Hacking (Data Dredging)

Description: Manipulating data or analyses until significant results emerge.

Manifestations:

• Collecting data until significance reached
• Testing multiple outcomes, reporting only significant ones
• Trying multiple analysis methods
• Excluding "outliers" to reach significance
• Subgroup analyses until finding significance

Detection:

• Clustering just below .05 can motivate further checks; it is not proof of p-hacking
• Many researcher degrees of freedom
• Undisclosed analyses
• Fishing expeditions

Mitigation:

• Preregister analysis plans
• Report all analyses conducted
• Correct for multiple comparisons
• Distinguish exploratory from confirmatory

20. HARKing (Hypothesizing After Results are Known)

Description: Presenting post hoc hypotheses as if they were predicted a priori.

Why problematic:

• Inflates apparent evidence
• Conflates exploration with confirmation
• Misrepresents the scientific process

Mitigation:

• Preregister hypotheses
• Clearly label exploratory analyses
• Require replication of unexpected findings

21. Base Rate Neglect

Description: Ignoring prior probability when evaluating evidence.

Example:

• With prevalence 1%, sensitivity 95%, and specificity 95%, the positive predictive value is 0.95*0.01 / (0.95*0.01 + 0.05*0.99) = 0.1610 (16.1%). Overall "accuracy" alone is insufficient; this teaching calculation assumes these quantities apply to the tested population.

Mitigation:

• Always consider base rates/prior probability
• Use Bayesian reasoning
• Report positive and negative predictive values

22. Regression to the Mean

Description: Extreme measurements tend to be followed by less extreme ones.

Manifestations:

• Treatment effects in extreme groups may be regression artifacts
• "Sophomore slump" in high performers

Mitigation:

• Use control groups
• Consider natural variation
• Don't select based on extreme baseline values without controls

23. Texas Sharpshooter Fallacy

Description: Selecting data after seeing patterns, like shooting arrows then drawing targets around clusters.

Manifestations:

• Finding patterns in random data
• Subgroup analyses selected post hoc
• Geographic clustering studies without correction

Mitigation:

• Prespecify hypotheses
• Correct for multiple comparisons
• Replicate findings in independent data

Reducing Bias: Best Practices

Study Design

1. Randomization
2. Blinding specified by role and information concealed
3. Control groups
4. Adequate sample size
5. Preregistration

Data Collection

1. Standardized protocols
2. Validated instruments
3. Objective measures when possible
4. Multiple observers/raters
5. Complete data collection

Analysis

1. Analysis aligned to assignment/adherence estimand, with explicit missing-data assumptions
2. Prespecified analyses
3. Appropriate statistical tests
4. Multiple comparison corrections
5. Sensitivity analyses

Reporting

1. Complete transparency
2. CONSORT, PRISMA, or similar guidelines
3. Report all outcomes
4. Distinguish exploratory from confirmatory
5. Share data and code

Meta-Level

1. Adversarial collaboration
2. Replication studies
3. Open science practices
4. Peer review
5. Systematic reviews