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| Criterion |  Operations PDCA Cycle |  Agile NoEstimates |  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. | When estimating slows a team down more than it helps, it shifts the focus to flow, small slices, and real lead time. It sorts work by value, risk, and delivery ability. The result is captured as Throughput Data, Flow Forecast, and Slicing Rules. | 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 | laufend | 1-4 Wochen | 30-60 min |
Participantsdifferent | 1-8 | 2-12 | 1-6 | 1-5 |
Formatdifferent | Workshop + async | Workshop + async | Async | Workshop + async |
Outputdifferent | PDCA Log, Experiment Plan, Learning Outcome, Standard Change | Throughput Data, Flow Forecast, Slicing Rules | Experiment results, Decision log, Learning summary | Completed Experiment Canvas, Success Metric |
Tagsno overlap | Continuous improvementLeanExperiments | EstimationForecastingFlow | ExperimentsGrowthAnalyticsValidation | ExperimentsValidationDiscoveryHypothesis |