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| Criterion |  Growth A/B Testing |  Agile Ideal Days |  Facilitation Dot Estimation |  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 effort needs to be thought of as real working time, it offers a simple common denominator for comparison. It sorts work by value, risk, and delivery ability. The result is captured as Ideal Day Estimates, Assumption Notes, and Capacity Caveats. | When size or effort can only be estimated roughly, Dot Estimation condenses the group's experience into a quick range. It turns individual contributions into a visible selection. The result is captured as an Effort Heatmap, Risk Signals, and Discussion Targets. | 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 | Low | Low | Low |
Timedifferent | 1-4 Wochen | 15-60 min | 5-20 min | 30-60 min |
Participantsdifferent | 1-6 | 2-9 | 3-20 | 1-5 |
Formatdifferent | Async | Workshop + async | Workshop | Workshop + async |
Outputdifferent | Experiment results, Decision log, Learning summary | Ideal Day Estimates, Assumption Notes, Capacity Caveats | Effort Heatmap, Risk Signals, Discussion Targets | Completed Experiment Canvas, Success Metric |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | EstimationEffortAgile | EstimationEffortRisk | ExperimentsValidationDiscoveryHypothesis |