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| Criterion |  Decision Making Decision Tree |  Product Discovery Fake Door Test |  Product Discovery Hypothesis Prioritization Canvas |  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 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. | When many hypotheses compete for attention, it brings order to their learning sequence and importance. It separates problem, assumption, solution, and evidence. The result is captured as a prioritization canvas and a hypothesis backlog. | 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. |
Complexitysame | Medium | Medium | Medium | Medium |
Timedifferent | 30-90 min | 1-5 Tage | 60-90 min | 45-75 min |
Participantsdifferent | 1-6 | Nutzertraffic | 3-8 | 2-8 |
Formatdifferent | Workshop + async | Async | Workshop | Workshop |
Outputdifferent | Decision Tree, Option Map, Assumption List | Click Data, Interest Signal, Learning Decision | Prioritization Canvas, Hypothesis Backlog | Test Matrix, Test Plan |
Tagsno overlap | DecisionTreeOptions | ValidationExperimentsDemandDiscovery | ExperimentsPrioritizationDiscoveryHypothesis | ExperimentsValidationDiscoveryOptions |