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| Criterion |  Growth A/B Testing |  Engineering Goal Question Metric |  Product Discovery Hypothesis Prioritization Canvas |  Product Discovery Fake Door Test |
|---|---|---|---|---|
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. | 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 many hypotheses compete for attention, it brings order to their learning sequence and importance. It separates problem, assumption, solution, and evidence. The result is captured as a prioritization canvas and a hypothesis backlog. | 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. |
Complexitydifferent | High | Medium | Medium | Medium |
Timedifferent | 1-4 Wochen | 90-180 min | 60-90 min | 1-5 Tage |
Participantsdifferent | 1-6 | 3-6 | 3-8 | Nutzertraffic |
Formatdifferent | Async | Workshop | Workshop | Async |
Outputdifferent | Experiment results, Decision log, Learning summary | GQM Table, Metric Profiles | Prioritization Canvas, Hypothesis Backlog | Click Data, Interest Signal, Learning Decision |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | MetricsMeasurementEngineeringAlignment | ExperimentsPrioritizationDiscoveryHypothesis | ValidationExperimentsDemandDiscovery |