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| Criterion |  Agile Affinity Estimation |  Operations PDCA Cycle |  Product Discovery Smoke Test |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | When many tasks need to be classified quickly, it sorts them by perceived effort and similarity. It reduces the effort of fine-grained estimation for large volumes. | 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 demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. | 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. |
Complexitydifferent | Medium | Low | Low | High |
Timedifferent | 30-90 min | 1 h bis mehrere Wochen | 1-5 Tage | 1-4 Wochen |
Participantsdifferent | 3-12 | 1-8 | Nutzertraffic | 1-6 |
Formatdifferent | Workshop | Workshop + async | Async | Async |
Outputdifferent | Affinity Size Map, Grouped Estimates, Unclear Items | PDCA Log, Experiment Plan, Learning Outcome, Standard Change | Interest Metrics, Conversion Signal, Learning Note | Experiment results, Decision log, Learning summary |
Tagsno overlap | EstimationBacklogRelative sizing | Continuous improvementLeanExperiments | ValidationExperimentsDemandGrowth | ExperimentsGrowthAnalyticsValidation |