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| Criterion |  Product Discovery MVP Test Matrix |  Decision Making Force Field Analysis |  Product Discovery Experiment Canvas |  Product Strategy DIBB |
|---|---|---|---|---|
Purposedifferent | When several minimal variants compete to answer the same question, it makes comparing and choosing between them cleaner. It separates problem, assumption, solution, and evidence. The result is captured as a Test Matrix and a Test Plan. | 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. | 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. |
Complexitydifferent | Medium | Low | Low | Low |
Timedifferent | 45-75 min | 45-90 min | 30-60 min | 1-2 h |
Participantsdifferent | 2-8 | 3-12 | 1-5 | 2-8 |
Formatdifferent | Workshop | Workshop | Workshop + async | Workshop + async |
Outputdifferent | Test Matrix, Test Plan | Force Field Map, Change Levers, Risk Notes | Completed Experiment Canvas, Success Metric | DIBB document, Belief list, Bet list, Learning report |
Tagsno overlap | ExperimentsValidationDiscoveryOptions | ChangeDecisionStrategy | ExperimentsValidationDiscoveryHypothesis | StrategyDecisionAssumptionsHypothesis |