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| Criterion |  Operations Scatter Diagram |  Operations Root Cause 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. | When a problem keeps recurring and the cause stays unclear, the method exposes the underlying mechanism. It separates symptom, guess, and robust explanation from one another. | 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 | 1-4 h | 1-5 Tage | 1-4 Wochen |
Participantsdifferent | 1-4 | 3-8 | Nutzertraffic | 1-6 |
Formatdifferent | Workshop + async | Workshop + async | Async | Async |
Outputdifferent | Scatter Plot, Korrelationsnotiz, Hypothesenliste | Problem Statement, Cause Hypotheses, Confirmed Causes, Action Plan | Interest Metrics, Conversion Signal, Learning Note | Experiment results, Decision log, Learning summary |
Tagsno overlap | Root causeQualityVisualization | Root causeProblem solvingQualityIncident | ValidationExperimentsDemandGrowth | ExperimentsGrowthAnalyticsValidation |