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| Criterion |  Decision Making PERT Estimation |  Product Discovery Opportunity Solution Tree |  Product Strategy DIBB |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
Purposedifferent | On complex initiatives, a plain average tends to understate just how uncertain the outcome really is. It separates options, evaluation criteria, and open risks. The result is captured as a PERT Estimate, Expected Value, and Risk Notes. | When discovery keeps swinging between goals, ideas, and learning loops, it creates clear steering logic. It connects the desired outcome, opportunities, and experiments into a legible structure. | 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. | 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 | Medium | Medium | Low | Low |
Timedifferent | 15-45 min | 1-2 h Setup, laufend | 1-2 h | 30-60 min |
Participantsdifferent | 1-8 | 2-6 | 2-8 | 1-5 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | PERT Estimate, Expected Value, Risk Notes | Opportunity Solution Tree, Experiment Backlog, Learning Log | DIBB document, Belief list, Bet list, Learning report | Completed Experiment Canvas, Success Metric |
Tagsno overlap | EstimationUncertaintyRisk | DiscoveryOutcomesExperimentsOpportunity | StrategyDecisionAssumptionsHypothesis | ExperimentsValidationDiscoveryHypothesis |