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| Criterion |  Growth A/B Testing |  Decision Making Force Field Analysis |  Product Discovery Riskiest Assumption Test |  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. | 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. | 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 | Low | Medium | Low |
Timedifferent | 1-4 Wochen | 45-90 min | 1-2 Wochen pro Iteration | 30-60 min |
Participantsdifferent | 1-6 | 3-12 | 2-6 | 1-5 |
Formatdifferent | Async | Workshop | Workshop + async | Workshop + async |
Outputdifferent | Experiment results, Decision log, Learning summary | Force Field Map, Change Levers, Risk Notes | Prioritized Assumption List, Test Plan, Results Report | Completed Experiment Canvas, Success Metric |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | ChangeDecisionStrategy | ExperimentsValidationDiscoveryAssumptions | ExperimentsValidationDiscoveryHypothesis |