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| Criterion |  Decision Making Delphi Method |  Product Discovery Hypothesis Prioritization Canvas |  Growth A/B Testing |  Product Discovery Fake Door Test |
|---|---|---|---|---|
Purposedifferent | When knowledge is distributed and direct dominance should be avoided, groups quickly fall under the sway of their opinion leaders. The Delphi Method gathers assessments iteratively and gradually brings expert judgments closer together. | 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 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 | High | Medium | High | Medium |
Timedifferent | 1-4 Wochen | 60-90 min | 1-4 Wochen | 1-5 Tage |
Participantsdifferent | 6-30 Experten | 3-8 | 1-6 | Nutzertraffic |
Formatdifferent | Async | Workshop | Async | Async |
Outputdifferent | Expert Forecast, Consensus Range, Assumption Notes | Prioritization Canvas, Hypothesis Backlog | Experiment results, Decision log, Learning summary | Click Data, Interest Signal, Learning Decision |
Tagsno overlap | ForecastingExpertsDecisionStrategy | ExperimentsPrioritizationDiscoveryHypothesis | ExperimentsGrowthAnalyticsValidation | ValidationExperimentsDemandDiscovery |