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| Criterion |  Growth A/B Testing |  Product Strategy DIBB |  Decision Making Force Field Analysis |  Product Discovery Riskiest Assumption Test |
|---|---|---|---|---|
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. | 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. | In change initiatives, supporting and restraining forces sit at the table at the same time. Force Field Analysis makes these tensions explicit and shows where change can be pushed forward by strengthening or relieving. | When an idea hinges on one critical unknown, it prioritizes testing that exact piece of uncertainty first. It separates problem, assumption, solution, and evidence. The result is captured as a prioritized list of assumptions, a test plan, and a result report. |
Complexitydifferent | High | Low | Low | Medium |
Timedifferent | 1-4 Wochen | 1-2 h | 45-90 min | 1-2 Wochen pro Iteration |
Participantsdifferent | 1-6 | 2-8 | 3-12 | 2-6 |
Formatdifferent | Async | Workshop + async | Workshop | Workshop + async |
Outputdifferent | Experiment results, Decision log, Learning summary | DIBB document, Belief list, Bet list, Learning report | Force Field Map, Change Levers, Risk Notes | Prioritized Assumption List, Test Plan, Results Report |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | StrategyDecisionAssumptionsHypothesis | ChangeDecisionStrategy | ExperimentsValidationDiscoveryAssumptions |