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| Criterion |  Engineering Hypothesis-Driven Troubleshooting |  Product Discovery Pretotyping |  Growth A/B Testing |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
Purposedifferent | When systems fail unexpectedly, spontaneous attempts often produce more noise than insight. Hypothesis-driven Troubleshooting translates symptoms into testable assumptions and makes troubleshooting learnable. | Pretotyping clarifies whether a customer problem and solution idea create enough real demand. It separates problem, assumption, solution, and evidence, and captures the result as a pretotyping sketch, test setup, and conversion data. | 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 | Low | High | Low |
Timedifferent | 30-240 min | Stunden bis wenige Tage | 1-4 Wochen | 30-60 min |
Participantsdifferent | 1-6 | 1-4 | 1-6 | 1-5 |
Formatdifferent | Workshop + async | Workshop + async | Async | Workshop + async |
Outputdifferent | Hypothesis Log, Test Plan, Evidence Notes, Diagnosis Summary | Pretotyping sketch, Test setup, Conversion data, Go or no-go decision | Experiment results, Decision log, Learning summary | Completed Experiment Canvas, Success Metric |
Tagsno overlap | TroubleshootingProblem solvingDiagnosis | ValidationDemandMVP | ExperimentsGrowthAnalyticsValidation | ExperimentsValidationDiscoveryHypothesis |