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| Criterion |  Product Strategy DIBB |  Decision Making Force Field Analysis |  Product Discovery Experiment Canvas |  Product Discovery Assumption Mapping |
|---|---|---|---|---|
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. | 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. | When assumptions still sit unordered in the room, it weighs uncertainty against leverage. It makes visible which hypotheses should be checked first. |
Complexitydifferent | Low | Low | Low | Medium |
Timedifferent | 1-2 h | 45-90 min | 30-60 min | 45-60 min |
Participantsdifferent | 2-8 | 3-12 | 1-5 | 2-8 |
Formatdifferent | Workshop + async | Workshop | Workshop + async | Workshop + async |
Outputdifferent | DIBB document, Belief list, Bet list, Learning report | Force Field Map, Change Levers, Risk Notes | Completed Experiment Canvas, Success Metric | Assumption map, Test backlog, Risk ranking |
Tagsno overlap | StrategyDecisionAssumptionsHypothesis | ChangeDecisionStrategy | ExperimentsValidationDiscoveryHypothesis | AssumptionsRiskExperimentsValidation |