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| Criterion |  Operations Scatter Diagram |  Product Discovery Assumption Mapping |  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. | When assumptions still sit unordered in the room, it weighs uncertainty against leverage. It makes visible which hypotheses should be checked first. | 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 | 45-60 min | 1-5 Tage | 1-4 Wochen |
Participantsdifferent | 1-4 | 2-8 | Nutzertraffic | 1-6 |
Formatdifferent | Workshop + async | Workshop + async | Async | Async |
Outputdifferent | Scatter Plot, Korrelationsnotiz, Hypothesenliste | Assumption map, Test backlog, Risk ranking | Interest Metrics, Conversion Signal, Learning Note | Experiment results, Decision log, Learning summary |
Tagsno overlap | Root causeQualityVisualization | AssumptionsRiskExperimentsValidation | ValidationExperimentsDemandGrowth | ExperimentsGrowthAnalyticsValidation |