| name | verify-a-statistical-claim |
| category | data |
| description | Check a numerical claim from its original data and definitions through calculation, uncertainty, and presentation. Use when a percentage, trend, comparison, or risk figure matters. |
verify-a-statistical-claim
Reconstruct the claim, not just the arithmetic. Most statistical errors begin with mismatched populations, time windows, denominators, or measures.
When to use
- Use for reported percentages, rates, averages, rankings, changes, correlations, forecasts, and risk statements.
- Increase rigor when decisions, money, health, safety, or public claims depend on the result.
Procedure
- Copy the exact claim and parse its numerator, denominator, unit, population, geography, period, comparison, and implied causal strength.
- Trace the number to the original dataset, table, analysis, or model and record its version.
- Read definitions, sampling, exclusions, weighting, missing-data handling, revisions, and uncertainty notes.
- Reproduce the calculation from the most granular authorized evidence available.
- Check percentage points versus percent change, nominal versus real values, counts versus rates, and mean versus median.
- Test alternative reasonable denominators, baselines, cutoffs, and time windows.
- Assess precision, confidence intervals or error bounds, sample size, and practical significance.
- Check whether aggregation hides subgroup differences or whether repeated observations are treated as independent.
- State the verdict: verified, approximately supported, misleading, unsupported, or not reproducible, with corrected wording.
Failure plan
- Do not infer causation from correlation or a trend from two selected endpoints.
- Do not fabricate missing data, weights, uncertainty, or source revisions.
- If only a chart image is available, label any extracted value approximate.
- Preserve privacy and disclosure limits when checking small groups.
Done
- The claim is traceable to a versioned source and reproducible calculation
- Definitions, denominator, uncertainty, sensitivity, and causal limits are explicit
- The recommended wording matches the evidence