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| Criterion |  Growth A/B Testing |  UX Research HEART Framework |  Product Discovery Fake Door 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. | Helps clarify observations, needs, and patterns in concrete terms. It groups observations into patterns, questions, and decisions. The result is captured as a HEART-GSM table and dashboard. | 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. | 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 | 120 min initial, dann laufend | 1-5 Tage | 30-60 min |
Participantsdifferent | 1-6 | 3-6 | Nutzertraffic | 1-5 |
Formatdifferent | Async | Workshop + async | Async | Workshop + async |
Outputdifferent | Experiment results, Decision log, Learning summary | HEART-GSM Table, Dashboard | Click Data, Interest Signal, Learning Decision | Completed Experiment Canvas, Success Metric |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | MetricsUX researchMeasurementSatisfaction | ValidationExperimentsDemandDiscovery | ExperimentsValidationDiscoveryHypothesis |