ds-verification-before-completion

To verify notebook results before making final analytical claims, rerun the relevant cells with stated parameters, confirm data identifiers and sample balance, recompute estimates and significance, and ensure artifacts match the written narrative.

Rerunning experiment analysis outputs before reporting metrics prevents stale, cached, or non-representative data from supporting decision-ready claims, ensuring your reported estimates, confidence intervals, and significance values are reproducible and exact.

Validating statistical significance requires explicit evidence inputs including data window, units, sample sizes, metric definition, and statistical method, while blocking screenshot-only p-values or unverified statements like the analysis ran yesterday.

To check consistency between data visualizations and written conclusions, recompute the estimate, interval, and significance metrics, then confirm that the charts, tables, and narrative text in your notebook all agree exactly.

You can make narrow, scoped intermediate progress claims without rerunning the entire analysis script by verifying only the specific artifacts and outputs that directly support that particular scoped claim.

Common failure patterns when validating metric reproducibility include relying on cached outputs, assuming results look right, using screenshot-only p-values, and ignoring order-dependent or brittle notebook cells that produce stale estimates.