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| Criterion |  Operations Fault Tree Analysis |  Growth A/B Testing |  Innovation Design Thinking |  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 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 complex user problems with an uncertain cause, the method connects observation, interpretation, and experiment into a learning cycle. It keeps the focus on real needs and avoids premature solution language. This produces robust decisions for product and service. | 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 | 2-6 h | 1-4 Wochen | 1 Tag bis mehrere Wochen | 1-5 Tage |
Participantsdifferent | 3-8 | 1-6 | 4-10 | Nutzertraffic |
Formatdifferent | Workshop + async | Async | Workshop | Async |
Outputdifferent | Fault Tree, Critical Paths, Cause Hypotheses, Control Actions | Experiment results, Decision log, Learning summary | Problem Statement, Prototype, Test Learnings | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | RiskRoot causeSafety | ExperimentsGrowthAnalyticsValidation | InnovationPrototypeResearch | ValidationExperimentsDemandGrowth |