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| Criterion |  Decision Making Decision Tree |  Product Discovery Experiment Canvas |  Growth A/B Testing |  Product Discovery Hypothesis Prioritization 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 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 many hypotheses compete for attention, it brings order to their learning sequence and importance. It separates problem, assumption, solution, and evidence. The result is captured as a prioritization canvas and a hypothesis backlog. |
Complexitydifferent | Medium | Low | High | Medium |
Timedifferent | 30-90 min | 30-60 min | 1-4 Wochen | 60-90 min |
Participantsdifferent | 1-6 | 1-5 | 1-6 | 3-8 |
Formatdifferent | Workshop + async | Workshop + async | Async | Workshop |
Outputdifferent | Decision Tree, Option Map, Assumption List | Completed Experiment Canvas, Success Metric | Experiment results, Decision log, Learning summary | Prioritization Canvas, Hypothesis Backlog |
Tagsno overlap | DecisionTreeOptions | ExperimentsValidationDiscoveryHypothesis | ExperimentsGrowthAnalyticsValidation | ExperimentsPrioritizationDiscoveryHypothesis |