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| Criterion |  Growth A/B Testing |  Agile Bucket System |  Product Discovery Smoke Test |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
Purposedifferent | 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 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 only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. | 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 | High | Medium | Low | Low |
Timedifferent | 1-4 Wochen | 30-90 min | 1-5 Tage | 30-60 min |
Participantsdifferent | 1-6 | 3-12 | Nutzertraffic | 1-5 |
Formatdifferent | Async | Workshop | Async | Workshop + async |
Outputdifferent | Experiment results, Decision log, Learning summary | Bucketed Backlog, Relative Estimates, Split Candidates | Interest Metrics, Conversion Signal, Learning Note | Completed Experiment Canvas, Success Metric |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | EstimationBacklogRelative sizing | ValidationExperimentsDemandGrowth | ExperimentsValidationDiscoveryHypothesis |