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| Criterion |  Operations Change Analysis |  Growth A/B Testing |  Delivery Cost of Delay |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
Purposedifferent | For a deviation after a change, the method isolates the influence of the altered condition. It narrows down which differences are relevant and which are just accompanying noise. | 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. | Helps clarify scope, order, and delivery flow in concrete terms. It makes work, constraints, and sequence manageable. The result is captured as a Cost of Delay table and prioritization sequence. | 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 | High | Low |
Timedifferent | 45-120 min | 1-4 Wochen | 90-180 min | 30-60 min |
Participantsdifferent | 2-6 | 1-6 | 3-8 | 1-5 |
Formatdifferent | Workshop + async | Async | Workshop | Workshop + async |
Outputdifferent | Change Matrix, Cause Hypotheses, Validation Questions, Action List | Experiment results, Decision log, Learning summary | CoD Table, Prioritization Sequence | Completed Experiment Canvas, Success Metric |
Tagsno overlap | ChangeRoot causeTroubleshootingComparison | ExperimentsGrowthAnalyticsValidation | PrioritizationDeliveryEconomicsDecision | ExperimentsValidationDiscoveryHypothesis |