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| Criterion |  Systems Thinking Leverage Points |  Growth A/B Testing |  Product Strategy DIBB |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
Purposedifferent | Leverage Points show exactly where interventions in a system produce a disproportionate effect. It draws relationships, patterns, and feedback loops. The result is captured as a leverage map and action strategy. | 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. | DIBB helps clarify target groups, value, goals, and priorities by making the logic behind a decision explicit. It captures results as a DIBB document, belief list, bet list, and learning report. | 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 | High | High | Low | Low |
Timedifferent | Half day | 1-4 Wochen | 1-2 h | 30-60 min |
Participantsdifferent | 3-12 | 1-6 | 2-8 | 1-5 |
Formatdifferent | Workshop | Async | Workshop + async | Workshop + async |
Outputdifferent | Leverage Map, Action Strategy | Experiment results, Decision log, Learning summary | DIBB document, Belief list, Bet list, Learning report | Completed Experiment Canvas, Success Metric |
Tagsno overlap | Systems thinkingChangeStrategy | ExperimentsGrowthAnalyticsValidation | StrategyDecisionAssumptionsHypothesis | ExperimentsValidationDiscoveryHypothesis |