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| Criterion |  Operations Fault Tree Analysis |  Growth A/B Testing |  UX Research Surveys |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
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. | When a topic needs to be validated broadly and many people can answer the same question, surveys gather structured feedback in a scalable form. Answers become comparable and segmentable instead of remaining merely anecdotal. | When experiments become unreadable after the fact, it arranges hypothesis, signal, and learning goal on a single canvas. It separates problem, assumption, solution, and evidence. The result is captured as a Completed Experiment Canvas and a Success Metric. |
Complexitydifferent | High | High | Medium | Low |
Timedifferent | 2-6 h | 1-4 Wochen | 3-14 Tage | 30-60 min |
Participantsdifferent | 3-8 | 1-6 | 50+ | 1-5 |
Formatdifferent | Workshop + async | Async | Async | Workshop + async |
Outputdifferent | Fault Tree, Critical Paths, Cause Hypotheses, Control Actions | Experiment results, Decision log, Learning summary | Survey Results, Charts, Segment Insights | Completed Experiment Canvas, Success Metric |
Tagsno overlap | RiskRoot causeSafety | ExperimentsGrowthAnalyticsValidation | QuantitativeResearchValidation | ExperimentsValidationDiscoveryHypothesis |