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| Criterion |  Product Strategy DIBB |  Decision Making Constraint Analysis |  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. | When an initiative stalls, the limit often lies not in the idea but in hard or soft boundary conditions. Constraint Analysis separates these limits and shows which of them can actually be shaped. | 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 | Low | Low | Medium |
Timedifferent | 1-2 h | 30-90 min | 30-60 min | 45-75 min |
Participantsdifferent | 2-8 | 2-8 | 1-5 | 2-8 |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Workshop |
Outputdifferent | DIBB document, Belief list, Bet list, Learning report | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries | Completed Experiment Canvas, Success Metric | Test Matrix, Test Plan |
Tagsno overlap | StrategyDecisionAssumptionsHypothesis | ConstraintsDecisionPlanningOptions | ExperimentsValidationDiscoveryHypothesis | ExperimentsValidationDiscoveryOptions |