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| Criterion |  Delivery Monte Carlo Forecasting |  Product Discovery Hypothesis Prioritization Canvas |  Product Discovery Fake Door Test |
|---|---|---|---|
Purposedifferent | Helps clarify scope, sequence, and delivery flow in concrete terms. It makes work, boundaries, and order steerable. The result is captured as Forecast Percentiles, Throughput Dataset, and Risk Communication. | 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 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 | Medium |
Timedifferent | 30-90 min Setup, danach laufend | 60-90 min | 1-5 Tage |
Participantsdifferent | 1-8 | 3-8 | Nutzertraffic |
Formatdifferent | Workshop + async | Workshop | Async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Prioritization Canvas, Hypothesis Backlog | Click Data, Interest Signal, Learning Decision |
Tagsno overlap | ForecastingFlowDelivery | ExperimentsPrioritizationDiscoveryHypothesis | ValidationExperimentsDemandDiscovery |
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