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| Criterion |  Decision Making Decision Tree |  Growth A/B Testing |  Decision Making Constraint Analysis |  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 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 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 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. |
Complexitydifferent | Medium | High | Low | Medium |
Timedifferent | 30-90 min | 1-4 Wochen | 30-90 min | 45-75 min |
Participantsdifferent | 1-6 | 1-6 | 2-8 | 2-8 |
Formatdifferent | Workshop + async | Async | Workshop + async | Workshop |
Outputdifferent | Decision Tree, Option Map, Assumption List | Experiment results, Decision log, Learning summary | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries | Test Matrix, Test Plan |
Tagsno overlap | DecisionTreeOptions | ExperimentsGrowthAnalyticsValidation | ConstraintsDecisionPlanningOptions | ExperimentsValidationDiscoveryOptions |