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| Criterion |  Growth A/B Testing |  Product Strategy RICE Scoring |  Product Discovery Fake Door Test |  Product Discovery Hypothesis Prioritization Canvas |
|---|---|---|---|---|
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 initiatives compete for the same resources, it brings reach, impact, confidence, and effort into one shared ranking. It makes prioritization economically connectable. | 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 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. |
Complexitydifferent | High | Medium | Medium | Medium |
Timedifferent | 1-4 Wochen | 60-90 min | 1-5 Tage | 60-90 min |
Participantsdifferent | 1-6 | 2-10 | Nutzertraffic | 3-8 |
Formatdifferent | Async | Workshop + async | Async | Workshop |
Outputdifferent | Experiment results, Decision log, Learning summary | RICE Scores, Ranked List, Assumption Log | Click Data, Interest Signal, Learning Decision | Prioritization Canvas, Hypothesis Backlog |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | PrioritizationScoringRoadmapTradeoffs | ValidationExperimentsDemandDiscovery | ExperimentsPrioritizationDiscoveryHypothesis |