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| Criterion |  Facilitation Dot Estimation |  Decision Making Decision Tree |  Product Discovery Smoke Test |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | 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. | 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 demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. | 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. |
Complexitydifferent | Low | Medium | Low | High |
Timedifferent | 5-20 min | 30-90 min | 1-5 Tage | 1-4 Wochen |
Participantsdifferent | 3-20 | 1-6 | Nutzertraffic | 1-6 |
Formatdifferent | Workshop | Workshop + async | Async | Async |
Outputdifferent | Effort Heatmap, Risk Signals, Discussion Targets | Decision Tree, Option Map, Assumption List | Interest Metrics, Conversion Signal, Learning Note | Experiment results, Decision log, Learning summary |
Tagsno overlap | EstimationEffortRisk | DecisionTreeOptions | ValidationExperimentsDemandGrowth | ExperimentsGrowthAnalyticsValidation |