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| Criterion |  Growth A/B Testing |  UX Research Surveys |  Product Discovery Assumption Mapping |  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 a topic needs to be validated broadly and many people can answer the same question, surveys gather structured feedback in a scalable form. Answers become comparable and segmentable instead of remaining merely anecdotal. | When assumptions still sit unordered in the room, it weighs uncertainty against leverage. It makes visible which hypotheses should be checked first. | 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 | 3-14 Tage | 45-60 min | 30-60 min |
Participantsdifferent | 1-6 | 50+ | 2-8 | 1-5 |
Formatdifferent | Async | Async | Workshop + async | Workshop + async |
Outputdifferent | Experiment results, Decision log, Learning summary | Survey Results, Charts, Segment Insights | Assumption map, Test backlog, Risk ranking | Completed Experiment Canvas, Success Metric |
Tags1 shared | ExperimentsGrowthAnalyticsValidation | QuantitativeResearchValidation | AssumptionsRiskExperimentsValidation | ExperimentsValidationDiscoveryHypothesis |