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| Criterion |  Decision Making Decision Tree |  Product Discovery Opportunity Solution Tree |  Product Discovery MVP Test Matrix |  Product Discovery Fake Door 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 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 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 demand is still unclear, it measures interest in a feature before it is built. It separates real behavior from polite agreement and deliberately keeps build effort small. |
Complexitysame | Medium | Medium | Medium | Medium |
Timedifferent | 30-90 min | 1-2 h Setup, laufend | 45-75 min | 1-5 Tage |
Participantsdifferent | 1-6 | 2-6 | 2-8 | Nutzertraffic |
Formatdifferent | Workshop + async | Workshop + async | Workshop | Async |
Outputdifferent | Decision Tree, Option Map, Assumption List | Opportunity Solution Tree, Experiment Backlog, Learning Log | Test Matrix, Test Plan | Click Data, Interest Signal, Learning Decision |
Tagsno overlap | DecisionTreeOptions | DiscoveryOutcomesExperimentsOpportunity | ExperimentsValidationDiscoveryOptions | ValidationExperimentsDemandDiscovery |