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| Criterion |  Agile Affinity Estimation |  Agile Bucket System |  Growth A/B Testing |  Product Discovery Fake Door Test |
|---|---|---|---|---|
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. | When a large batch of work needs a fast, rough estimate, it sorts items into predefined size buckets. It sorts work by value, risk, and delivery ability. The result is captured as a Bucketed Backlog, Relative Estimates, and Split Candidates. | 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 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. |
Complexitydifferent | Medium | Medium | High | Medium |
Timedifferent | 30-90 min | 30-90 min | 1-4 Wochen | 1-5 Tage |
Participantsdifferent | 3-12 | 3-12 | 1-6 | Nutzertraffic |
Formatdifferent | Workshop | Workshop | Async | Async |
Outputdifferent | Affinity Size Map, Grouped Estimates, Unclear Items | Bucketed Backlog, Relative Estimates, Split Candidates | Experiment results, Decision log, Learning summary | Click Data, Interest Signal, Learning Decision |
Tagsno overlap | EstimationBacklogRelative sizing | EstimationBacklogRelative sizing | ExperimentsGrowthAnalyticsValidation | ValidationExperimentsDemandDiscovery |