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| Criterion |  Growth A/B Testing |  Product Discovery Smoke Test |  Knowledge Modeling Competency Questions |  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. | Competency Questions translate a domain model into concrete questions that it must be able to answer. The method keeps the model's scope clean and prevents pretty but useless structures. | 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 | 1-5 Tage | 2-4 h | 30-60 min |
Participantsdifferent | 1-6 | Nutzertraffic | 2-8 | 1-5 |
Formatdifferent | Async | Async | Workshop + async | Workshop + async |
Outputdifferent | Experiment results, Decision log, Learning summary | Interest Metrics, Conversion Signal, Learning Note | Competency Question Set, Required Concepts, Test Queries, Coverage Matrix | Completed Experiment Canvas, Success Metric |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | ValidationExperimentsDemandGrowth | OntologyKnowledge graphScopeRequirementsSemantic | ExperimentsValidationDiscoveryHypothesis |