How do you detect false negatives or false positives in tests?
Detecting unreliable test results:
Cross-reference sources: Compare seed results to postmaster data and engagement metrics. Consistent signals across sources increase confidence.
Multiple test runs: Single tests can be anomalous. Repeated testing reveals whether results are stable.
Panel validation: If available, compare seed results to panel-based data from real users.
Engagement reality check: If seeds show spam but engagement metrics remain strong, seeds may be showing false negatives.
No single source is definitive. Triangulation reveals the truth.
When your instruments disagree, take multiple readings and trust the consensus.
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