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| Criterion |  Growth A/B Testing |  Product Discovery Smoke Test |  Decision Making Decision Tree |  Decision Making Constraint Analysis |
|---|---|---|---|---|
Purposedifferent | 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 demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. | 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 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. |
Complexitydifferent | High | Low | Medium | Low |
Timedifferent | 1-4 Wochen | 1-5 Tage | 30-90 min | 30-90 min |
Participantsdifferent | 1-6 | Nutzertraffic | 1-6 | 2-8 |
Formatdifferent | Async | Async | Workshop + async | Workshop + async |
Outputdifferent | Experiment results, Decision log, Learning summary | Interest Metrics, Conversion Signal, Learning Note | Decision Tree, Option Map, Assumption List | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | ValidationExperimentsDemandGrowth | DecisionTreeOptions | ConstraintsDecisionPlanningOptions |