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| Criterion |  Product Strategy DIBB |  Decision Making Decision Tree |  Product Discovery Experiment Canvas |  Product Discovery MVP Test Matrix |
|---|---|---|---|---|
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. | For decisions with follow-on paths and dependencies, a linear comparison is often not enough. A Decision Tree shows how options branch under conditions and which consequences hang on each branch. | 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 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. |
Complexitydifferent | Low | Medium | Low | Medium |
Timedifferent | 1-2 h | 30-90 min | 30-60 min | 45-75 min |
Participantsdifferent | 2-8 | 1-6 | 1-5 | 2-8 |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Workshop |
Outputdifferent | DIBB document, Belief list, Bet list, Learning report | Decision Tree, Option Map, Assumption List | Completed Experiment Canvas, Success Metric | Test Matrix, Test Plan |
Tagsno overlap | StrategyDecisionAssumptionsHypothesis | DecisionTreeOptions | ExperimentsValidationDiscoveryHypothesis | ExperimentsValidationDiscoveryOptions |