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| Criterion |  Operations Fault Tree Analysis |  Product Discovery Fake Door Test |  Growth A/B Testing |  Product Discovery Smoke Test |
|---|---|---|---|---|
Purposedifferent | For a critical top event with several possible triggers, the method logically models failure paths. It makes visible which combinations of conditions can lead to damage. | When demand is still unclear, it measures interest in a feature before it is built. It separates real behavior from polite agreement and deliberately keeps build effort small. | 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. | 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 | Medium | High | Low |
Timedifferent | 2-6 h | 1-5 Tage | 1-4 Wochen | 1-5 Tage |
Participantsdifferent | 3-8 | Nutzertraffic | 1-6 | Nutzertraffic |
Formatdifferent | Workshop + async | Async | Async | Async |
Outputdifferent | Fault Tree, Critical Paths, Cause Hypotheses, Control Actions | Click Data, Interest Signal, Learning Decision | Experiment results, Decision log, Learning summary | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | RiskRoot causeSafety | ValidationExperimentsDemandDiscovery | ExperimentsGrowthAnalyticsValidation | ValidationExperimentsDemandGrowth |