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| Criterion |  Agile Story Splitting |  Growth A/B Testing |  Agile Affinity Estimation |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
Purposedifferent | When a story becomes too large for a clean flow, it breaks scope down along value and risk. It shapes the work into a form that ships earlier and is easier to verify. | 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 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 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 | High | Medium | Low |
Timedifferent | 30-60 min | 1-4 Wochen | 30-90 min | 30-60 min |
Participantsdifferent | 2-6 | 1-6 | 3-12 | 1-5 |
Formatdifferent | Workshop + async | Async | Workshop | Workshop + async |
Outputdifferent | Smaller Stories, Acceptance Criteria, Split Rationale | Experiment results, Decision log, Learning summary | Affinity Size Map, Grouped Estimates, Unclear Items | Completed Experiment Canvas, Success Metric |
Tagsno overlap | BacklogIterationDelivery | ExperimentsGrowthAnalyticsValidation | EstimationBacklogRelative sizing | ExperimentsValidationDiscoveryHypothesis |