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| Criterion |  Decision Making Decision Tree |  Growth A/B Testing |  Product Strategy ICE Scoring |  Product Discovery Smoke Test |
|---|---|---|---|---|
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 demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. |
Complexitydifferent | Medium | High | Low | Low |
Timedifferent | 30-90 min | 1-4 Wochen | 30-60 min | 1-5 Tage |
Participantsdifferent | 1-6 | 1-6 | 2-8 | Nutzertraffic |
Formatdifferent | Workshop + async | Async | Workshop + async | Async |
Outputdifferent | Decision Tree, Option Map, Assumption List | Experiment results, Decision log, Learning summary | ICE Table, Top Idea List | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | DecisionTreeOptions | ExperimentsGrowthAnalyticsValidation | PrioritizationScoringGrowthDecision | ValidationExperimentsDemandGrowth |