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| Criterion |  Growth A/B Testing |  Product Strategy ICE Scoring |  Product Discovery Experiment Canvas |  Product Discovery Fake Door Test |
|---|---|---|---|---|
Purposedifferent | 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 ideas need sorting quickly, it condenses impact, confidence, and effort into one compact score. It connects customer value, product logic, and decision priorities. The result is captured as an ICE table and top-ideas list. | 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. | 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. |
Complexitydifferent | High | Low | Low | Medium |
Timedifferent | 1-4 Wochen | 30-60 min | 30-60 min | 1-5 Tage |
Participantsdifferent | 1-6 | 2-8 | 1-5 | Nutzertraffic |
Formatdifferent | Async | Workshop + async | Workshop + async | Async |
Outputdifferent | Experiment results, Decision log, Learning summary | ICE Table, Top Idea List | Completed Experiment Canvas, Success Metric | Click Data, Interest Signal, Learning Decision |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | PrioritizationScoringGrowthDecision | ExperimentsValidationDiscoveryHypothesis | ValidationExperimentsDemandDiscovery |