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| Criterion |  Decision Making Decision Tree |  Growth A/B Testing |  Decision Making Assumption Surfacing |  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 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. | Plans often contain assumptions that were never openly stated and remain dangerous precisely because of that. Assumption Surfacing makes these silent premises visible and prioritizes which of them carry the initiative or could sink it. | 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 | High | Low | Low |
Timedifferent | 30-90 min | 1-4 Wochen | 45-90 min | 30-60 min |
Participantsdifferent | 1-6 | 1-6 | 2-8 | 1-5 |
Formatdifferent | Workshop + async | Async | Workshop + async | Workshop + async |
Outputdifferent | Decision Tree, Option Map, Assumption List | Experiment results, Decision log, Learning summary | Assumption List, Critical Assumptions, Learning Plan | Completed Experiment Canvas, Success Metric |
Tagsno overlap | DecisionTreeOptions | ExperimentsGrowthAnalyticsValidation | AssumptionsRiskDecisionDiscovery | ExperimentsValidationDiscoveryHypothesis |