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| Criterion |  Growth A/B Testing |  Operations Root Cause Tree Analysis |  Operations Change Analysis |  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 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. | For a deviation after a change, the method isolates the influence of the altered condition. It narrows down which differences are relevant and which are just accompanying noise. | 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 | 1-3 h | 45-120 min | 1-5 Tage |
Participantsdifferent | 1-6 | 2-8 | 2-6 | Nutzertraffic |
Formatdifferent | Async | Workshop | Workshop + async | Async |
Outputdifferent | Experiment results, Decision log, Learning summary | Cause Tree, Evidence Notes, Countermeasures | Change Matrix, Cause Hypotheses, Validation Questions, Action List | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | Root causeTreeIncidentQuality | ChangeRoot causeTroubleshootingComparison | ValidationExperimentsDemandGrowth |