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| Criterion |  Growth A/B Testing |  Product Discovery Pretotyping |  Product Discovery Hypothesis Prioritization Canvas |  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. | Pretotyping clarifies whether a customer problem and solution idea create enough real demand. It separates problem, assumption, solution, and evidence, and captures the result as a pretotyping sketch, test setup, and conversion data. | 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 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 | Low | Medium | Low |
Timedifferent | 1-4 Wochen | Stunden bis wenige Tage | 60-90 min | 30-60 min |
Participantsdifferent | 1-6 | 1-4 | 3-8 | 1-5 |
Formatdifferent | Async | Workshop + async | Workshop | Workshop + async |
Outputdifferent | Experiment results, Decision log, Learning summary | Pretotyping sketch, Test setup, Conversion data, Go or no-go decision | Prioritization Canvas, Hypothesis Backlog | Completed Experiment Canvas, Success Metric |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | ValidationDemandMVP | ExperimentsPrioritizationDiscoveryHypothesis | ExperimentsValidationDiscoveryHypothesis |