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| Criterion |  Operations PDCA Cycle |  Decision Making PERT Estimation |  Growth A/B Testing |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
Purposedifferent | For an improvement that still has to prove itself in everyday work, the method runs it through small learning loops. It connects planning, checking, and standardization into a repeatable learning mode. | On complex initiatives, a plain average tends to understate just how uncertain the outcome really is. It separates options, evaluation criteria, and open risks. The result is captured as a PERT Estimate, Expected Value, and Risk Notes. | 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 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 | Low | Medium | High | Low |
Timedifferent | 1 h bis mehrere Wochen | 15-45 min | 1-4 Wochen | 30-60 min |
Participantsdifferent | 1-8 | 1-8 | 1-6 | 1-5 |
Formatdifferent | Workshop + async | Workshop + async | Async | Workshop + async |
Outputdifferent | PDCA Log, Experiment Plan, Learning Outcome, Standard Change | PERT Estimate, Expected Value, Risk Notes | Experiment results, Decision log, Learning summary | Completed Experiment Canvas, Success Metric |
Tagsno overlap | Continuous improvementLeanExperiments | EstimationUncertaintyRisk | ExperimentsGrowthAnalyticsValidation | ExperimentsValidationDiscoveryHypothesis |