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| Criterion |  Growth A/B Testing |  Product Discovery Smoke Test |  Engineering Fault Isolation |  Growth Funnel Analysis |
|---|---|---|---|---|
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. | 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. | In technical failures, the visible symptom often gets mixed up with the actual cause. Fault Isolation narrows the fault space and progressively reduces which part of the system is truly affected. | When many visitors or users drop off along the way, the reason behind the number often stays hidden. Funnel Analysis exposes these transitions and makes visible exactly where the path collapses. |
Complexitydifferent | High | Low | Medium | Medium |
Timedifferent | 1-4 Wochen | 1-5 Tage | 30-180 min | 1-3 h |
Participantsdifferent | 1-6 | Nutzertraffic | 1-6 | 1-5 |
Formatdifferent | Async | Async | Workshop + async | Async |
Outputdifferent | Experiment results, Decision log, Learning summary | Interest Metrics, Conversion Signal, Learning Note | Fault Isolation Map, Test Log, Excluded Hypotheses, Narrowed Fault Area | Funnel report, Drop-off analysis, Optimization hypotheses |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | ValidationExperimentsDemandGrowth | TroubleshootingDiagnosisEngineering | AnalyticsConversionGrowth |