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| Criterion |  Product Discovery Hypothesis Prioritization Canvas |  Product Strategy DIBB |  Decision Making Force Field Analysis |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
Purposedifferent | 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. | 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. | In change initiatives, supporting and restraining forces sit at the table at the same time. Force Field Analysis makes these tensions explicit and shows where change can be pushed forward by strengthening or relieving. | 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 | Low | Low | Low |
Timedifferent | 60-90 min | 1-2 h | 45-90 min | 30-60 min |
Participantsdifferent | 3-8 | 2-8 | 3-12 | 1-5 |
Formatdifferent | Workshop | Workshop + async | Workshop | Workshop + async |
Outputdifferent | Prioritization Canvas, Hypothesis Backlog | DIBB document, Belief list, Bet list, Learning report | Force Field Map, Change Levers, Risk Notes | Completed Experiment Canvas, Success Metric |
Tagsno overlap | ExperimentsPrioritizationDiscoveryHypothesis | StrategyDecisionAssumptionsHypothesis | ChangeDecisionStrategy | ExperimentsValidationDiscoveryHypothesis |