Common Statistical Errors and How to Avoid Them
2026-05-04 • 6 min
Quick Answer
Common statistical errors include outcome switching, assumption violations, unplanned subgroup testing, and selective reporting. Prevent them by predefining analysis plans, checking diagnostics, documenting deviations, and presenting complete results. Structured quality checks improve reproducibility and protect publication credibility.
Design-Stage Pitfalls
Unclear hypotheses, ambiguous endpoints, and absent power justification cause downstream analysis and interpretation failures.
Analysis-Stage Pitfalls
Ignoring assumptions, overfitting, and uncorrected multiple testing increase false discoveries and unstable conclusions.
Reporting-Stage Pitfalls
Selective reporting and missing diagnostics reduce trust. Transparent methods and complete outputs strengthen evidence quality.
Frequently Asked Questions
What is one high-impact prevention strategy?
Use a preregistered or protocol-defined analysis plan before viewing final outcomes.
Do assumption checks need to be reported?
Yes. Reporting diagnostics supports validity and helps reviewers interpret model reliability.