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| Criterion |  Product Discovery Hypothesis Prioritization Canvas |  Agile Bucket System |  Product Discovery Fake Door Test |  Agile Affinity Estimation |
|---|---|---|---|---|
Purposedifferent | When many hypotheses compete for attention, it brings order to their learning sequence and importance. It separates problem, assumption, solution, and evidence. The result is captured as a prioritization canvas and a hypothesis backlog. | 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. | 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. |
Complexitysame | Medium | Medium | Medium | Medium |
Timedifferent | 60-90 min | 30-90 min | 1-5 Tage | 30-90 min |
Participantsdifferent | 3-8 | 3-12 | Nutzertraffic | 3-12 |
Formatdifferent | Workshop | Workshop | Async | Workshop |
Outputdifferent | Prioritization Canvas, Hypothesis Backlog | Bucketed Backlog, Relative Estimates, Split Candidates | Click Data, Interest Signal, Learning Decision | Affinity Size Map, Grouped Estimates, Unclear Items |
Tagsno overlap | ExperimentsPrioritizationDiscoveryHypothesis | EstimationBacklogRelative sizing | ValidationExperimentsDemandDiscovery | EstimationBacklogRelative sizing |