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| Criterion |  Decision Making Decision Tree |  Product Discovery Experiment Canvas |  Decision Making Constraint Analysis |  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 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 an initiative stalls, the limit often lies not in the idea but in hard or soft boundary conditions. Constraint Analysis separates these limits and shows which of them can actually be shaped. | 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 | 30-60 min | 30-90 min | 1-5 Tage |
Participantsdifferent | 1-6 | 1-5 | 2-8 | Nutzertraffic |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Async |
Outputdifferent | Decision Tree, Option Map, Assumption List | Completed Experiment Canvas, Success Metric | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries | Click Data, Interest Signal, Learning Decision |
Tagsno overlap | DecisionTreeOptions | ExperimentsValidationDiscoveryHypothesis | ConstraintsDecisionPlanningOptions | ValidationExperimentsDemandDiscovery |