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| Criterion |  Growth A/B Testing |  Systems Thinking Causal Loop Diagram |  Decision Making Assumption Surfacing |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
Purposedifferent | 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. | A Causal Loop Diagram makes feedback, reinforcement, and balance in a system legible. It uncovers side effects and self-reinforcement that stay hidden in linear explanations. | 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 | High | Medium | Low | Low |
Timedifferent | 1-4 Wochen | 1-3 h | 45-90 min | 30-60 min |
Participantsdifferent | 1-6 | 2-8 | 2-8 | 1-5 |
Formatdifferent | Async | Workshop | Workshop + async | Workshop + async |
Outputdifferent | Experiment results, Decision log, Learning summary | Causal Loop Diagram, Feedback Notes, Leverage Points | Assumption List, Critical Assumptions, Learning Plan | Completed Experiment Canvas, Success Metric |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | FeedbackSystems thinkingCausalityDynamics | AssumptionsRiskDecisionDiscovery | ExperimentsValidationDiscoveryHypothesis |