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| Criterion |  Growth A/B Testing |  Product Strategy DIBB |  Product Discovery Fake Door 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. | DIBB helps clarify target groups, value, goals, and priorities by making the logic behind a decision explicit. It captures results as a DIBB document, belief list, bet list, and learning report. | 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. |
Complexitydifferent | High | Low | Medium |
Timedifferent | 1-4 Wochen | 1-2 h | 1-5 Tage |
Participantsdifferent | 1-6 | 2-8 | Nutzertraffic |
Formatdifferent | Async | Workshop + async | Async |
Outputdifferent | Experiment results, Decision log, Learning summary | DIBB document, Belief list, Bet list, Learning report | Click Data, Interest Signal, Learning Decision |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | StrategyDecisionAssumptionsHypothesis | ValidationExperimentsDemandDiscovery |
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