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| Criterion |  Agile Affinity Estimation |  Growth Growth Experiment |  Product Discovery Experiment Canvas |  Growth A/B Testing |
|---|---|---|---|---|
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 growth ideas surface quickly, the temptation to build immediately instead of learning is high. A Growth Experiment frames an assumption so target group, lever, and measurement are clear before the first deployment. | 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 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. |
Complexitydifferent | Medium | Medium | Low | High |
Timedifferent | 30-90 min | 1-2 Wochen | 30-60 min | 1-4 Wochen |
Participantsdifferent | 3-12 | 1-6 | 1-5 | 1-6 |
Formatdifferent | Workshop | Workshop + async | Workshop + async | Async |
Outputdifferent | Affinity Size Map, Grouped Estimates, Unclear Items | Experiment card, Result summary, Next bet | Completed Experiment Canvas, Success Metric | Experiment results, Decision log, Learning summary |
Tagsno overlap | EstimationBacklogRelative sizing | MarketingGrowthExperimentsLearning | ExperimentsValidationDiscoveryHypothesis | ExperimentsGrowthAnalyticsValidation |