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| Criterion |  Product Discovery MVP Test Matrix |  Product Discovery Opportunity Solution Tree |  Growth A/B Testing |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
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. | When discovery keeps swinging between goals, ideas, and learning loops, it creates clear steering logic. It connects the desired outcome, opportunities, and experiments into a legible structure. | When two variants compete, discussions quickly decide by taste rather than effect. A/B Testing checks behavior under controlled conditions and separates real improvement from chance or expectation effects. | 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 | Medium | High | Low |
Timedifferent | 45-75 min | 1-2 h Setup, laufend | 1-4 Wochen | 30-60 min |
Participantsdifferent | 2-8 | 2-6 | 1-6 | 1-5 |
Formatdifferent | Workshop | Workshop + async | Async | Workshop + async |
Outputdifferent | Test Matrix, Test Plan | Opportunity Solution Tree, Experiment Backlog, Learning Log | Experiment results, Decision log, Learning summary | Completed Experiment Canvas, Success Metric |
Tags1 shared | ExperimentsValidationDiscoveryOptions | DiscoveryOutcomesExperimentsOpportunity | ExperimentsGrowthAnalyticsValidation | ExperimentsValidationDiscoveryHypothesis |