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| Criterion |  Product Discovery Experiment Canvas |  Agile Affinity Estimation |  Agile Bucket System |  Product Discovery Fake Door Test |
|---|---|---|---|---|
Purposedifferent | 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. | 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 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 | Low | Medium | Medium | Medium |
Timedifferent | 30-60 min | 30-90 min | 30-90 min | 1-5 Tage |
Participantsdifferent | 1-5 | 3-12 | 3-12 | Nutzertraffic |
Formatdifferent | Workshop + async | Workshop | Workshop | Async |
Outputdifferent | Completed Experiment Canvas, Success Metric | Affinity Size Map, Grouped Estimates, Unclear Items | Bucketed Backlog, Relative Estimates, Split Candidates | Click Data, Interest Signal, Learning Decision |
Tagsno overlap | ExperimentsValidationDiscoveryHypothesis | EstimationBacklogRelative sizing | EstimationBacklogRelative sizing | ValidationExperimentsDemandDiscovery |