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| Criterion |  Product Strategy DIBB |  Delivery Monte Carlo Forecasting |  Product Discovery Hypothesis Prioritization Canvas |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
Purposedifferent | DIBB helps clarify target groups, value, goals, and priorities by making the logic behind a decision explicit. It captures results as a DIBB document, belief list, bet list, and learning report. | 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 experiments become unreadable after the fact, it arranges hypothesis, signal, and learning goal on a single canvas. It separates problem, assumption, solution, and evidence. The result is captured as a Completed Experiment Canvas and a Success Metric. |
Complexitydifferent | Low | High | Medium | Low |
Timedifferent | 1-2 h | 30-90 min Setup, danach laufend | 60-90 min | 30-60 min |
Participantsdifferent | 2-8 | 1-8 | 3-8 | 1-5 |
Formatdifferent | Workshop + async | Workshop + async | Workshop | Workshop + async |
Outputdifferent | DIBB document, Belief list, Bet list, Learning report | Forecast Percentiles, Throughput Dataset, Risk Communication | Prioritization Canvas, Hypothesis Backlog | Completed Experiment Canvas, Success Metric |
Tagsno overlap | StrategyDecisionAssumptionsHypothesis | ForecastingFlowDelivery | ExperimentsPrioritizationDiscoveryHypothesis | ExperimentsValidationDiscoveryHypothesis |