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| Criterion |  Delivery Monte Carlo Forecasting |  Product Discovery Learning Card |  Product Discovery Assumption Mapping |  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. | The Learning Card connects traceable test results to a reasoned next action while keeping the limits of the insight visible. | When assumptions still sit unordered in the room, it weighs uncertainty against leverage. It makes visible which hypotheses should be checked first. | 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 | Low | Medium | Medium |
Timedifferent | 30-90 min Setup, danach laufend | 25-40 min | 45-60 min | 1-5 Tage |
Participantsdifferent | 1-8 | 1-5 | 2-8 | Nutzertraffic |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Learning Card with evidence and next action | Assumption map, Test backlog, Risk ranking | Click Data, Interest Signal, Learning Decision |
Tagsno overlap | ForecastingFlowDelivery | ExperimentsValidationDiscoveryLearning | AssumptionsRiskExperimentsValidation | ValidationExperimentsDemandDiscovery |