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| Criterion |  Operations Scatter Diagram |  Operations Root Cause Tree Analysis |  Product Discovery Smoke Test |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | When the question is whether two measures are related, the method makes patterns in the data picture visible. It separates felt correlations from relationships that actually show up in the data. | For a problem with several suspected causes, the method builds a causal structure instead of a mere list. It makes visible how causes connect and where the strongest points of leverage lie. | 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 | 30-90 min | 1-3 h | 1-5 Tage | 1-4 Wochen |
Participantsdifferent | 1-4 | 2-8 | Nutzertraffic | 1-6 |
Formatdifferent | Workshop + async | Workshop | Async | Async |
Outputdifferent | Scatter Plot, Korrelationsnotiz, Hypothesenliste | Cause Tree, Evidence Notes, Countermeasures | Interest Metrics, Conversion Signal, Learning Note | Experiment results, Decision log, Learning summary |
Tagsno overlap | Root causeQualityVisualization | Root causeTreeIncidentQuality | ValidationExperimentsDemandGrowth | ExperimentsGrowthAnalyticsValidation |