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.
Key takeaways
- Many statistical failures begin with an unclear question, endpoint, or unit of analysis—not with software.
- Dependencies, clustering, repeated measurements, and sampling design must be represented in the analysis.
- Exploratory analyses are valuable when they are labeled honestly and not presented as pre-specified confirmation.
- A reproducible workflow and complete reporting make errors easier to find before peer review.
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.
Practical method
Step-by-Step Workflow
- 1
Audit the design
Check the research question, primary outcome, sampling unit, comparison groups, allocation, power reasoning, and potential sources of bias.
- 2
Audit the data
Reconcile the cohort, missingness, exclusions, duplicates, outcome timing, coding, and whether the recorded unit matches the unit analyzed.
- 3
Audit the model
Review assumptions, complexity relative to information, multiplicity, influence, validation, and sensitivity to defensible alternative specifications.
- 4
Audit the report
Ensure methods reproduce results and that estimates, intervals, denominators, diagnostics, deviations, null findings, and limitations are visible.
Worked example
A false-positive subgroup story
- Scenario
- A study tests 20 subgroups without a prior hypothesis and finds one interaction with p = 0.04.
- Approach
- Label the finding exploratory, report how many interactions were tested, show the interaction estimate and interval, and seek confirmation in independent data rather than highlighting one subgroup in isolation.
- Interpretation
- With many tests, at least one small p-value can appear by chance. Biological plausibility and replication matter more than whether a single result crossed 0.05.
Common Mistakes to Avoid
- Analyzing observations as independent when they are clustered
- Fitting too many parameters for the available information
- Selecting outcomes or subgroups after seeing results
- Hiding exclusions, deviations, or null findings
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.