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| Criterion |  Growth A/B Testing |  Engineering Failure Scenario Analysis |  Decision Making Constraint Analysis |  Product Discovery MVP Test Matrix |
|---|---|---|---|---|
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 several minimal variants compete to answer the same question, it makes comparing and choosing between them cleaner. It separates problem, assumption, solution, and evidence. The result is captured as a Test Matrix and a Test Plan. |
Complexitydifferent | High | Medium | Low | Medium |
Timedifferent | 1-4 Wochen | 1-3 h | 30-90 min | 45-75 min |
Participantsdifferent | 1-6 | 3-8 | 2-8 | 2-8 |
Formatdifferent | Async | Workshop | Workshop + async | Workshop |
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 | Test Matrix, Test Plan |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | FailureResilienceRisk | ConstraintsDecisionPlanningOptions | ExperimentsValidationDiscoveryOptions |