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| Criterion |  Engineering Failure Scenario Analysis |  Innovation Design Sprint |  Growth A/B Testing |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
Purposedifferent | In critical processes, it is not enough to plan only for the normal case. Failure Scenario Analysis looks at the path into failure and shows which failures, chains, and control gaps cause the most damage. | When the product question is unclear and time pressure is high, the method takes a team from an open idea to a testable solution. It connects problem understanding, decision, and learning in one compact format, lowering the risk of spending a lot of energy on a mere guess. | 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 | 1-3 h | 4-5 Tage | 1-4 Wochen | 30-60 min |
Participantsdifferent | 3-8 | 5-8 | 1-6 | 1-5 |
Formatdifferent | Workshop | Workshop | Async | Workshop + async |
Outputdifferent | Failure Scenarios, Risk Notes, Control Gaps, Test and Response Actions | Prototype, Test Findings, Decision Rationale | Experiment results, Decision log, Learning summary | Completed Experiment Canvas, Success Metric |
Tagsno overlap | FailureResilienceRisk | Design sprintPrototypeValidationInnovation | ExperimentsGrowthAnalyticsValidation | ExperimentsValidationDiscoveryHypothesis |