View methods side by side.
Choose up to four methods. Add them using the search and share the comparison by copying its link.
| Criterion |  Product Discovery Experiment Canvas |  Growth A/B Testing |  Domain Modeling EventStorming |  Product Discovery Fake Door Test |
|---|---|---|---|---|
Purposedifferent | 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. | 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 domain consists of many events, rules, and states, it creates a shared modeling space for the team. It bundles language, flows, and boundaries before domain knowledge fragments into siloed views. | 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. |
Complexitydifferent | Low | High | Medium | Medium |
Timedifferent | 30-60 min | 1-4 Wochen | 2-8 h | 1-5 Tage |
Participantsdifferent | 1-5 | 1-6 | 5-12 | Nutzertraffic |
Formatdifferent | Workshop + async | Async | Workshop | Async |
Outputdifferent | Completed Experiment Canvas, Success Metric | Experiment results, Decision log, Learning summary | Event Timeline, Ubiquitous Language, Boundaries, Open Questions | Click Data, Interest Signal, Learning Decision |
Tagsno overlap | ExperimentsValidationDiscoveryHypothesis | ExperimentsGrowthAnalyticsValidation | Domain-Driven DesignEventsDiscoveryWorkshop | ValidationExperimentsDemandDiscovery |