stress-test

A stress test for an analysis plan is a pre-execution sanity check that identifies methodological flaws like flawed baselines, survivorship bias, and missing segments so teams avoid wasted time and misleading conclusions during data work.

You check an analysis plan by applying a 7-point diagnostic that evaluates data availability, hypothesis clarity, baseline validity, survivorship bias, segment coverage, confounds, kill criteria, and output alignment to surface blockers before committing resources.

Yes, data validation stress tests require the active dataset schema to be available at .knowledge/datasets/{active}/schema.md to verify required data exists and confirm hypotheses against actual available fields.

Yes, you can review any documented analysis plan, stakeholder brief, or analysis-design document regardless of origin to detect survivorship bias, missing segments, and unaddressed kill criteria before execution.

A methodology review report includes structured PASS, WARNING, and FAIL verdicts across seven diagnostic points, detailing specific blockers and recommended fixes for fast stakeholder decision-making.

The best way to identify confounds and unaddressed kill criteria is enforcing a structured 7-point stress test that actively checks output alignment and methodological rigor before you execute the analysis.