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| Criterion |  Growth A/B Testing |  Product Discovery Hypothesis Prioritization Canvas |  Product Strategy DIBB |  Product Discovery Assumption Mapping |
|---|---|---|---|---|
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. | When many hypotheses compete for attention, it brings order to their learning sequence and importance. It separates problem, assumption, solution, and evidence. The result is captured as a prioritization canvas and a hypothesis backlog. | 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 assumptions still sit unordered in the room, it weighs uncertainty against leverage. It makes visible which hypotheses should be checked first. |
Complexitydifferent | High | Medium | Low | Medium |
Timedifferent | 1-4 Wochen | 60-90 min | 1-2 h | 45-60 min |
Participantsdifferent | 1-6 | 3-8 | 2-8 | 2-8 |
Formatdifferent | Async | Workshop | Workshop + async | Workshop + async |
Outputdifferent | Experiment results, Decision log, Learning summary | Prioritization Canvas, Hypothesis Backlog | DIBB document, Belief list, Bet list, Learning report | Assumption map, Test backlog, Risk ranking |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | ExperimentsPrioritizationDiscoveryHypothesis | StrategyDecisionAssumptionsHypothesis | AssumptionsRiskExperimentsValidation |