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| Criterion |  Operations Scatter Diagram |  Product Discovery Smoke Test |  Operations Root Cause Analysis |  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 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 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 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 | Low | Medium | High |
Timedifferent | 30-90 min | 1-5 Tage | 1-4 h | 1-4 Wochen |
Participantsdifferent | 1-4 | Nutzertraffic | 3-8 | 1-6 |
Formatdifferent | Workshop + async | Async | Workshop + async | Async |
Outputdifferent | Scatter Plot, Korrelationsnotiz, Hypothesenliste | Interest Metrics, Conversion Signal, Learning Note | Problem Statement, Cause Hypotheses, Confirmed Causes, Action Plan | Experiment results, Decision log, Learning summary |
Tagsno overlap | Root causeQualityVisualization | ValidationExperimentsDemandGrowth | Root causeProblem solvingQualityIncident | ExperimentsGrowthAnalyticsValidation |