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| Criterion |  Growth A/B Testing |  Delivery Monte Carlo Forecasting |  Product Discovery Assumption Mapping |
|---|---|---|---|
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. | 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 assumptions still sit unordered in the room, it weighs uncertainty against leverage. It makes visible which hypotheses should be checked first. |
Complexitydifferent | High | High | Medium |
Timedifferent | 1-4 Wochen | 30-90 min Setup, danach laufend | 45-60 min |
Participantsdifferent | 1-6 | 1-8 | 2-8 |
Formatdifferent | Async | Workshop + async | Workshop + async |
Outputdifferent | Experiment results, Decision log, Learning summary | Forecast Percentiles, Throughput Dataset, Risk Communication | Assumption map, Test backlog, Risk ranking |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | ForecastingFlowDelivery | AssumptionsRiskExperimentsValidation |
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