View methods side by side.
Choose up to four methods. Add them using the search and share the comparison by copying its link.
| Criterion |  Decision Making Decision Tree |  Product Discovery Fake Door Test |  Growth A/B Testing |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
Purposedifferent | For decisions with follow-on paths and dependencies, a linear comparison is often not enough. A Decision Tree shows how options branch under conditions and which consequences hang on each branch. | 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 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 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 | Medium | Medium | High | Low |
Timedifferent | 30-90 min | 1-5 Tage | 1-4 Wochen | 30-60 min |
Participantsdifferent | 1-6 | Nutzertraffic | 1-6 | 1-5 |
Formatdifferent | Workshop + async | Async | Async | Workshop + async |
Outputdifferent | Decision Tree, Option Map, Assumption List | Click Data, Interest Signal, Learning Decision | Experiment results, Decision log, Learning summary | Completed Experiment Canvas, Success Metric |
Tagsno overlap | DecisionTreeOptions | ValidationExperimentsDemandDiscovery | ExperimentsGrowthAnalyticsValidation | ExperimentsValidationDiscoveryHypothesis |