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| Criterion |  Product Discovery Hypothesis Prioritization Canvas |  Product Discovery Experiment Canvas |  Growth A/B Testing |  Product Discovery Fake Door Test |
|---|---|---|---|---|
Purposedifferent | 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. | When experiments become unreadable after the fact, it arranges hypothesis, signal, and learning goal on a single canvas. It separates problem, assumption, solution, and evidence. The result is captured as a Completed Experiment Canvas and a Success Metric. | 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. |
Complexitydifferent | Medium | Low | High | Medium |
Timedifferent | 60-90 min | 30-60 min | 1-4 Wochen | 1-5 Tage |
Participantsdifferent | 3-8 | 1-5 | 1-6 | Nutzertraffic |
Formatdifferent | Workshop | Workshop + async | Async | Async |
Outputdifferent | Prioritization Canvas, Hypothesis Backlog | Completed Experiment Canvas, Success Metric | Experiment results, Decision log, Learning summary | Click Data, Interest Signal, Learning Decision |
Tags1 shared | ExperimentsPrioritizationDiscoveryHypothesis | ExperimentsValidationDiscoveryHypothesis | ExperimentsGrowthAnalyticsValidation | ValidationExperimentsDemandDiscovery |