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| Criterion |  Growth A/B Testing |  Domain Modeling Context Map |  Product Discovery Fake Door Test |  Product Discovery MVP Test Matrix |
|---|---|---|---|---|
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 several contexts need to talk to each other, it makes their relationships and dependencies legible. It helps sort integration pressure and responsibilities across system boundaries. | When demand is still unclear, it measures interest in a feature before it is built. It separates real behavior from polite agreement and deliberately keeps build effort small. | When several minimal variants compete to answer the same question, it makes comparing and choosing between them cleaner. It separates problem, assumption, solution, and evidence. The result is captured as a Test Matrix and a Test Plan. |
Complexitydifferent | High | Medium | Medium | Medium |
Timedifferent | 1-4 Wochen | 1-3 h | 1-5 Tage | 45-75 min |
Participantsdifferent | 1-6 | 2-8 | Nutzertraffic | 2-8 |
Formatdifferent | Async | Workshop + async | Async | Workshop |
Outputdifferent | Experiment results, Decision log, Learning summary | Context Map, Integration Patterns, Boundary Notes | Click Data, Interest Signal, Learning Decision | Test Matrix, Test Plan |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | Domain-Driven DesignBoundariesStrategy | ValidationExperimentsDemandDiscovery | ExperimentsValidationDiscoveryOptions |