What to check before reporting AI-generated statistics
Fluent code and explanations require the same independent checks as any other analysis, plus careful verification of invented assumptions.
Fluent code and explanations require the same independent checks as any other analysis, plus careful verification of invented assumptions.
Verify the question and every input
AI may silently infer variable meaning, reference levels, missing-value rules, or study design. Compare each assumption with the actual protocol and data dictionary.
Run and inspect the complete workflow
Check package behavior, preprocessing order, leakage, grouped observations, random seeds, diagnostics, warnings, and whether displayed output came from the shown code.
Match the prose to the result
Confirm coefficient scale, direction, uncertainty, comparison group, prediction horizon, and limitations. Plausible language is not evidence.
Failure modes to check
- Choosing a method because it is familiar rather than because it matches the estimand and data structure.
- Treating assumptions as a generic pass/fail checklist instead of evidence about a specific model.
- Changing the workflow after seeing results without recording the decision or its effect on uncertainty.
- Reporting software output without tracing the claim to final code, diagnostics, and limitations.
Continue the decision path
Explore the related statistical consulting service, or discuss the decision in your own study.
Related decision guides
- Analysis-plan to output consistency checklist
- How to choose effect-size inputs for a defensible power analysis
Methodological note
This guide provides general educational information. The appropriate decision depends on the study question, design, data, evidence, ethics, and institutional requirements.