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| Criterion |  Product Discovery Smoke Test |  Knowledge Modeling Ontology Design Patterns |  Product Discovery Riskiest Assumption Test |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | 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. | Ontology Design Patterns provide reusable building blocks for recurring modeling problems. The method brings structure to the search for clean concepts, relationships, and roles. | When an idea hinges on one critical unknown, it prioritizes testing that exact piece of uncertainty first. It separates problem, assumption, solution, and evidence. The result is captured as a prioritized list of assumptions, a test plan, and a result report. | 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. |
Complexitydifferent | Low | Medium | Medium | High |
Timedifferent | 1-5 Tage | 1-3 h pro Pattern | 1-2 Wochen pro Iteration | 1-4 Wochen |
Participantsdifferent | Nutzertraffic | 1-6 | 2-6 | 1-6 |
Formatdifferent | Async | Workshop + async | Workshop + async | Async |
Outputdifferent | Interest Metrics, Conversion Signal, Learning Note | Selected Patterns, Adapted Schema Fragments, Pattern Documentation | Prioritized Assumption List, Test Plan, Results Report | Experiment results, Decision log, Learning summary |
Tagsno overlap | ValidationExperimentsDemandGrowth | OntologyPatternsReuseSemanticModeling | ExperimentsValidationDiscoveryAssumptions | ExperimentsGrowthAnalyticsValidation |