calibrate

Validating scoring constants against known decision outcomes requires replaying historical cases to recompute evidence weight, confidence, and verdicts, then comparing predicted outcomes with actual results to identify calibration bias and false positives.

Product evaluation confidence feels too hot or too cold when scoring constants are misaligned with reality, causing false positives or false negatives that require threshold validation and confidence tuning against historical decision data.

Tuning confidence thresholds using historical product decisions involves applying one-lever-at-a-time changes to scoring constants, generating local tuning recommendations that prepare a team-scoped override profile without altering shipped default scoring rules.

Decision analysis calibration requires a minimum of ten closed product decisions with known outcomes to effectively test whether confidence is overestimating opportunity quality before applying changes to scoring thresholds.

Adjusting product evaluation scoring without altering the default profile is possible by preparing a team-scoped override profile that keeps recommended calibration changes local, ensuring shipped default scoring constants remain unchanged.

Diagnosing false positives in product evaluation scoring is best achieved by running outcome-based validation that replays past decisions against known results, identifying the dominant calibration bias in the current scoring setup.