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| Criterion |  Growth A/B Testing |  Product Discovery Smoke Test |  UX Research Card Sorting |  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 only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. | When content only makes sense internally and users can't find the structure again, card sorting exposes their mental order. Terms, groups, and naming are then aligned with the target group's expectations. | 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 | Low | Low |
Timedifferent | 1-4 Wochen | 1-5 Tage | 20-45 min | 30-60 min |
Participantsdifferent | 1-6 | Nutzertraffic | Based on research question | 1-5 |
Formatdifferent | Async | Async | Workshop + async | Workshop + async |
Outputdifferent | Experiment results, Decision log, Learning summary | Interest Metrics, Conversion Signal, Learning Note | Content groups, Label set, IA hypotheses | Completed Experiment Canvas, Success Metric |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | ValidationExperimentsDemandGrowth | Information architectureNavigationStructure | ExperimentsValidationDiscoveryHypothesis |