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| Criterion |  Operations Scatter Diagram |  Decision Making Pareto Analysis |  Product Discovery Assumption Mapping |  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. | In many problem situations, effect is distributed unequally across many causes. A Pareto Analysis separates the few drivers with high leverage from the long remainder and focuses attention on what measurably moves the needle. | When assumptions still sit unordered in the room, it weighs uncertainty against leverage. It makes visible which hypotheses should be checked first. | 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 | 30-60 min | 45-60 min | 1-4 Wochen |
Participantsdifferent | 1-4 | 1-6 | 2-8 | 1-6 |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Async |
Outputdifferent | Scatter Plot, Korrelationsnotiz, Hypothesenliste | Pareto Chart, Top Causes, Focus List | Assumption map, Test backlog, Risk ranking | Experiment results, Decision log, Learning summary |
Tagsno overlap | Root causeQualityVisualization | PrioritizationQualityFocusRoot cause | AssumptionsRiskExperimentsValidation | ExperimentsGrowthAnalyticsValidation |