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| Criterion |  Agile Affinity Estimation |  Operations PDCA Cycle |  Product Discovery Fake Door Test |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
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 is still unclear, it measures interest in a feature before it is built. It separates real behavior from polite agreement and deliberately keeps build effort small. | 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 | Medium | Low | Medium | Low |
Timedifferent | 30-90 min | 1 h bis mehrere Wochen | 1-5 Tage | 30-60 min |
Participantsdifferent | 3-12 | 1-8 | Nutzertraffic | 1-5 |
Formatdifferent | Workshop | Workshop + async | Async | Workshop + async |
Outputdifferent | Affinity Size Map, Grouped Estimates, Unclear Items | PDCA Log, Experiment Plan, Learning Outcome, Standard Change | Click Data, Interest Signal, Learning Decision | Completed Experiment Canvas, Success Metric |
Tagsno overlap | EstimationBacklogRelative sizing | Continuous improvementLeanExperiments | ValidationExperimentsDemandDiscovery | ExperimentsValidationDiscoveryHypothesis |