View methods side by side.
Choose up to four methods. Add them using the search and share the comparison by copying its link.
| Criterion |  Operations Change Analysis |  Operations Fault Tree Analysis |  Growth A/B Testing |  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. | For a critical top event with several possible triggers, the method logically models failure paths. It makes visible which combinations of conditions can lead to damage. | 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 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 | 2-6 h | 1-4 Wochen | 30-60 min |
Participantsdifferent | 2-6 | 3-8 | 1-6 | 1-5 |
Formatdifferent | Workshop + async | Workshop + async | Async | Workshop + async |
Outputdifferent | Change Matrix, Cause Hypotheses, Validation Questions, Action List | Fault Tree, Critical Paths, Cause Hypotheses, Control Actions | Experiment results, Decision log, Learning summary | Completed Experiment Canvas, Success Metric |
Tagsno overlap | ChangeRoot causeTroubleshootingComparison | RiskRoot causeSafety | ExperimentsGrowthAnalyticsValidation | ExperimentsValidationDiscoveryHypothesis |