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| Criterion |  Product Strategy DIBB |  Growth A/B Testing |  Product Discovery Fake Door Test |  Product Discovery Riskiest Assumption Test |
|---|---|---|---|---|
Purposedifferent | 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 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 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. | When an idea hinges on one critical unknown, it prioritizes testing that exact piece of uncertainty first. It separates problem, assumption, solution, and evidence. The result is captured as a prioritized list of assumptions, a test plan, and a result report. |
Complexitydifferent | Low | High | Medium | Medium |
Timedifferent | 1-2 h | 1-4 Wochen | 1-5 Tage | 1-2 Wochen pro Iteration |
Participantsdifferent | 2-8 | 1-6 | Nutzertraffic | 2-6 |
Formatdifferent | Workshop + async | Async | Async | Workshop + async |
Outputdifferent | DIBB document, Belief list, Bet list, Learning report | Experiment results, Decision log, Learning summary | Click Data, Interest Signal, Learning Decision | Prioritized Assumption List, Test Plan, Results Report |
Tagsno overlap | StrategyDecisionAssumptionsHypothesis | ExperimentsGrowthAnalyticsValidation | ValidationExperimentsDemandDiscovery | ExperimentsValidationDiscoveryAssumptions |