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| Criterion |  Growth A/B Testing |  Product Strategy RICE Scoring |  Product Discovery Experiment Canvas |  Product Strategy ICE Scoring |
|---|---|---|---|---|
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 many initiatives compete for the same resources, it brings reach, impact, confidence, and effort into one shared ranking. It makes prioritization economically connectable. | 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 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. |
Complexitydifferent | High | Medium | Low | Low |
Timedifferent | 1-4 Wochen | 60-90 min | 30-60 min | 30-60 min |
Participantsdifferent | 1-6 | 2-10 | 1-5 | 2-8 |
Formatdifferent | Async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Experiment results, Decision log, Learning summary | RICE Scores, Ranked List, Assumption Log | Completed Experiment Canvas, Success Metric | ICE Table, Top Idea List |
Tagsno overlap | ExperimentsGrowthAnalyticsValidation | PrioritizationScoringRoadmapTradeoffs | ExperimentsValidationDiscoveryHypothesis | PrioritizationScoringGrowthDecision |