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| Criterion |  Decision Making Decision Tree |  Growth A/B Testing |  Product Strategy ICE Scoring |  Product Discovery MVP Test Matrix |
|---|---|---|---|---|
Purposedifferent | 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 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 ideas need sorting quickly, it condenses impact, confidence, and effort into one compact score. It connects customer value, product logic, and decision priorities. The result is captured as an ICE table and top-ideas list. | 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 | Medium | High | Low | Medium |
Timedifferent | 30-90 min | 1-4 Wochen | 30-60 min | 45-75 min |
Participantsdifferent | 1-6 | 1-6 | 2-8 | 2-8 |
Formatdifferent | Workshop + async | Async | Workshop + async | Workshop |
Outputdifferent | Decision Tree, Option Map, Assumption List | Experiment results, Decision log, Learning summary | ICE Table, Top Idea List | Test Matrix, Test Plan |
Tagsno overlap | DecisionTreeOptions | ExperimentsGrowthAnalyticsValidation | PrioritizationScoringGrowthDecision | ExperimentsValidationDiscoveryOptions |