Outlier Cases

Cases where one part differs from the rest. We evaluate whether the anomaly is isolated, functionally relevant, and large enough to change the final batch decision.

Outlier Decision Matrix

A single anomaly does not automatically reject a batch. We score outliers by repeatability risk, functional impact, and evidence strength.

Signal Typical Reading Action
Isolated visual defect1 of 8 partsDocument exception, release with note
Fit or load failureAny single partHold batch, expand sample
Pattern recurrence2+ matching anomaliesTreat as process drift
One Part Failed, Seven Did Not
Case: Functional Outlier

One Part Failed, Seven Did Not

This review asks whether one failing part is random contamination or a first warning of repeatability loss in the run.

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A Later Sample Changed the Final Decision
Case: Late-Batch Signal

A Later Sample Changed the Final Decision

An outlier appeared only in later samples, changing acceptance logic and proving that timing matters in small-batch evidence.

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