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| Criterion |  Decision Making Decision Tree |  Facilitation Dot Estimation |  Growth A/B Testing |  Product Discovery Smoke 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 size or effort can only be estimated roughly, Dot Estimation condenses the group's experience into a quick range. It turns individual contributions into a visible selection. The result is captured as an Effort Heatmap, Risk Signals, and Discussion Targets. | 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. |
Complexitydifferent | Medium | Low | High | Low |
Timedifferent | 30-90 min | 5-20 min | 1-4 Wochen | 1-5 Tage |
Participantsdifferent | 1-6 | 3-20 | 1-6 | Nutzertraffic |
Formatdifferent | Workshop + async | Workshop | Async | Async |
Outputdifferent | Decision Tree, Option Map, Assumption List | Effort Heatmap, Risk Signals, Discussion Targets | Experiment results, Decision log, Learning summary | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | DecisionTreeOptions | EstimationEffortRisk | ExperimentsGrowthAnalyticsValidation | ValidationExperimentsDemandGrowth |