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| Criterion |  Growth A/B Testing |  Growth Funnel Analysis |  Product Discovery Smoke Test |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
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 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. | 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 experiments become unreadable after the fact, it arranges hypothesis, signal, and learning goal on a single canvas. It separates problem, assumption, solution, and evidence. The result is captured as a Completed Experiment Canvas and a Success Metric. |
Complexitydifferent | High | Medium | Low | Low |
Timedifferent | 1-4 Wochen | 1-3 h | 1-5 Tage | 30-60 min |
Participantsdifferent | 1-6 | 1-5 | Nutzertraffic | 1-5 |
Formatdifferent | Async | Async | Async | Workshop + async |
Outputdifferent | Experiment results, Decision log, Learning summary | Funnel report, Drop-off analysis, Optimization hypotheses | Interest Metrics, Conversion Signal, Learning Note | Completed Experiment Canvas, Success Metric |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | AnalyticsConversionGrowth | ValidationExperimentsDemandGrowth | ExperimentsValidationDiscoveryHypothesis |