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| Criterion |  Growth A/B Testing |  Operations PDCA Cycle |  Product Discovery MVP Test Matrix |  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. | 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 several minimal variants compete to answer the same question, it makes comparing and choosing between them cleaner. It separates problem, assumption, solution, and evidence. The result is captured as a Test Matrix and a Test Plan. | 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 | 1 h bis mehrere Wochen | 45-75 min | 30-60 min |
Participantsdifferent | 1-6 | 1-8 | 2-8 | 1-5 |
Formatdifferent | Async | Workshop + async | Workshop | Workshop + async |
Outputdifferent | Experiment results, Decision log, Learning summary | PDCA Log, Experiment Plan, Learning Outcome, Standard Change | Test Matrix, Test Plan | Completed Experiment Canvas, Success Metric |
Tags1 shared | ExperimentsGrowthAnalyticsValidation | Continuous improvementLeanExperiments | ExperimentsValidationDiscoveryOptions | ExperimentsValidationDiscoveryHypothesis |