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| Criterion |  Engineering Hypothesis-Driven Troubleshooting |  Product Discovery Smoke Test |  Growth Growth Experiment |  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 demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. | 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 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 | Low | Medium | High |
Timedifferent | 30-240 min | 1-5 Tage | 1-2 Wochen | 1-4 Wochen |
Participantsdifferent | 1-6 | Nutzertraffic | 1-6 | 1-6 |
Formatdifferent | Workshop + async | Async | Workshop + async | Async |
Outputdifferent | Hypothesis Log, Test Plan, Evidence Notes, Diagnosis Summary | Interest Metrics, Conversion Signal, Learning Note | Experiment card, Result summary, Next bet | Experiment results, Decision log, Learning summary |
Tagsno overlap | TroubleshootingProblem solvingDiagnosis | ValidationExperimentsDemandGrowth | MarketingGrowthExperimentsLearning | ExperimentsGrowthAnalyticsValidation |