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| Criterion |  Engineering Hypothesis-Driven Troubleshooting |  Growth Growth Experiment |  Product Discovery Fake Door Test |  Growth A/B Testing |
|---|---|---|---|---|
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. | When growth ideas surface quickly, the temptation to build immediately instead of learning is high. A Growth Experiment frames an assumption so target group, lever, and measurement are clear before the first deployment. | When demand is still unclear, it measures interest in a feature before it is built. It separates real behavior from polite agreement and deliberately keeps build effort small. | 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. |
Complexitydifferent | Medium | Medium | Medium | High |
Timedifferent | 30-240 min | 1-2 Wochen | 1-5 Tage | 1-4 Wochen |
Participantsdifferent | 1-6 | 1-6 | Nutzertraffic | 1-6 |
Formatdifferent | Workshop + async | Workshop + async | Async | Async |
Outputdifferent | Hypothesis Log, Test Plan, Evidence Notes, Diagnosis Summary | Experiment card, Result summary, Next bet | Click Data, Interest Signal, Learning Decision | Experiment results, Decision log, Learning summary |
Tagsno overlap | TroubleshootingProblem solvingDiagnosis | MarketingGrowthExperimentsLearning | ValidationExperimentsDemandDiscovery | ExperimentsGrowthAnalyticsValidation |