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| Criterion |  Growth A/B Testing |  Product Discovery Fake Door Test |  Engineering Goal Question Metric |  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 demand is still unclear, it measures interest in a feature before it is built. It separates real behavior from polite agreement and deliberately keeps build effort small. | Helps clarify technical problems, hypotheses, and next steps in concrete terms. It breaks a technical problem into testable parts. The result is captured as a GQM table and metric briefs. | 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 | Medium | Low |
Timedifferent | 1-4 Wochen | 1-5 Tage | 90-180 min | 30-60 min |
Participantsdifferent | 1-6 | Nutzertraffic | 3-6 | 1-5 |
Formatdifferent | Async | Async | Workshop | Workshop + async |
Outputdifferent | Experiment results, Decision log, Learning summary | Click Data, Interest Signal, Learning Decision | GQM Table, Metric Profiles | Completed Experiment Canvas, Success Metric |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | ValidationExperimentsDemandDiscovery | MetricsMeasurementEngineeringAlignment | ExperimentsValidationDiscoveryHypothesis |