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| Criterion |  Agile Bucket System |  Product Strategy DIBB |  Product Discovery Experiment Canvas |  Agile Affinity Estimation |
|---|---|---|---|---|
Purposedifferent | 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. | DIBB helps clarify target groups, value, goals, and priorities by making the logic behind a decision explicit. It captures results as a DIBB document, belief list, bet list, and learning report. | 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. |
Complexitydifferent | Medium | Low | Low | Medium |
Timedifferent | 30-90 min | 1-2 h | 30-60 min | 30-90 min |
Participantsdifferent | 3-12 | 2-8 | 1-5 | 3-12 |
Formatdifferent | Workshop | Workshop + async | Workshop + async | Workshop |
Outputdifferent | Bucketed Backlog, Relative Estimates, Split Candidates | DIBB document, Belief list, Bet list, Learning report | Completed Experiment Canvas, Success Metric | Affinity Size Map, Grouped Estimates, Unclear Items |
Tagsno overlap | EstimationBacklogRelative sizing | StrategyDecisionAssumptionsHypothesis | ExperimentsValidationDiscoveryHypothesis | EstimationBacklogRelative sizing |