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| Criterion |  Decision Making Decision Tree |  Product Discovery Smoke Test |  Product Discovery Experiment Canvas |  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 demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. | 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. | 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. |
Complexitydifferent | Medium | Low | Low | Medium |
Timedifferent | 30-90 min | 1-5 Tage | 30-60 min | 1-5 Tage |
Participantsdifferent | 1-6 | Nutzertraffic | 1-5 | Nutzertraffic |
Formatdifferent | Workshop + async | Async | Workshop + async | Async |
Outputdifferent | Decision Tree, Option Map, Assumption List | Interest Metrics, Conversion Signal, Learning Note | Completed Experiment Canvas, Success Metric | Click Data, Interest Signal, Learning Decision |
Tagsno overlap | DecisionTreeOptions | ValidationExperimentsDemandGrowth | ExperimentsValidationDiscoveryHypothesis | ValidationExperimentsDemandDiscovery |