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| Criterion |  Growth A/B Testing |  Engineering Failure Scenario Analysis |  Decision Making Constraint 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. | In critical processes, it is not enough to plan only for the normal case. Failure Scenario Analysis looks at the path into failure and shows which failures, chains, and control gaps cause the most damage. | When an initiative stalls, the limit often lies not in the idea but in hard or soft boundary conditions. Constraint Analysis separates these limits and shows which of them can actually be shaped. | 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 | Low | Low |
Timedifferent | 1-4 Wochen | 1-3 h | 30-90 min | 1-5 Tage |
Participantsdifferent | 1-6 | 3-8 | 2-8 | Nutzertraffic |
Formatdifferent | Async | Workshop | Workshop + async | Async |
Outputdifferent | Experiment results, Decision log, Learning summary | Failure Scenarios, Risk Notes, Control Gaps, Test and Response Actions | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | FailureResilienceRisk | ConstraintsDecisionPlanningOptions | ValidationExperimentsDemandGrowth |