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| Criterion |  Growth A/B Testing |  Operations Causal Factor Analysis |  Operations MORT Analysis |  Product Discovery Smoke Test |
|---|---|---|---|---|
Purposedifferent | When two variants compete, discussions quickly decide by taste rather than effect. A/B Testing checks behavior under controlled conditions and separates real improvement from chance or expectation effects. | For an event with a complicated course, the method breaks down the contributing factors along the timeline. It shows how conditions, decisions, and reactions together produce a course of events. | For a safety-relevant event or a system with high protection requirements, the method examines where controls failed. It exposes both technical and organizational gaps. | When demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. |
Complexitydifferent | High | High | High | Low |
Timedifferent | 1-4 Wochen | 2-6 h | Mehrere Tage bis Wochen | 1-5 Tage |
Participantsdifferent | 1-6 | 3-10 | 2-6 | Nutzertraffic |
Formatdifferent | Async | Workshop + async | Workshop + async | Async |
Outputdifferent | Experiment results, Decision log, Learning summary | Event Timeline, Causal Factor Chart, Cause List, Corrective Actions | MORT Worksheets, Findings per Branch, Corrective Actions, Systemic Recommendations | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | CausalityIncidentRoot causeTimeline | Root causeSafetySystemicIncident | ValidationExperimentsDemandGrowth |