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| Criterion |  Product Discovery Hypothesis Prioritization Canvas |  Growth Pirate Metrics AARRR |  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. | For products with complex usage paths, overall growth alone is too coarse to reveal bottlenecks. Pirate Metrics breaks the relationship with the product into consecutive stages and shows where the funnel actually leaks. | 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 | Medium | High | Medium |
Timedifferent | 60-90 min | 1-2 h Setup, laufend | 1-4 Wochen | 1-5 Tage |
Participantsdifferent | 3-8 | 2-8 | 1-6 | Nutzertraffic |
Formatdifferent | Workshop | Workshop + async | Async | Async |
Outputdifferent | Prioritization Canvas, Hypothesis Backlog | AARRR funnel, Metric baseline, Experiment backlog | Experiment results, Decision log, Learning summary | Click Data, Interest Signal, Learning Decision |
Tags1 shared | ExperimentsPrioritizationDiscoveryHypothesis | GrowthMetricsExperiments | ExperimentsGrowthAnalyticsValidation | ValidationExperimentsDemandDiscovery |