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| Criterion |  Growth A/B Testing |  Decision Making Decision Tree |  Growth Growth Experiment |  Product Discovery Smoke Test |
|---|---|---|---|---|
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. | 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 growth ideas surface quickly, the temptation to build immediately instead of learning is high. A Growth Experiment frames an assumption so target group, lever, and measurement are clear before the first deployment. | 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. |
Complexitydifferent | High | Medium | Medium | Low |
Timedifferent | 1-4 Wochen | 30-90 min | 1-2 Wochen | 1-5 Tage |
Participantsdifferent | 1-6 | 1-6 | 1-6 | Nutzertraffic |
Formatdifferent | Async | Workshop + async | Workshop + async | Async |
Outputdifferent | Experiment results, Decision log, Learning summary | Decision Tree, Option Map, Assumption List | Experiment card, Result summary, Next bet | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | DecisionTreeOptions | MarketingGrowthExperimentsLearning | ValidationExperimentsDemandGrowth |