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| Criterion |  Growth A/B Testing |  Product Discovery Smoke Test |  Knowledge Modeling Shape-First Modeling |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
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 demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. | Shape-First Modeling defines data quality through shapes before implementation or integration frays at the edges. The approach fits when validation and data contracts should be part of the design from the start. | 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 | High | Low | Medium | Low |
Timedifferent | 1-4 Wochen | 1-5 Tage | Halber Tag pro Domain Slice | 30-60 min |
Participantsdifferent | 1-6 | Nutzertraffic | 1-4 | 1-5 |
Formatdifferent | Async | Async | Workshop + async | Workshop + async |
Outputdifferent | Experiment results, Decision log, Learning summary | Interest Metrics, Conversion Signal, Learning Note | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Completed Experiment Canvas, Success Metric |
Tags1 shared | ExperimentsGrowthAnalyticsValidation | ValidationExperimentsDemandGrowth | Knowledge graphValidationSemantic | ExperimentsValidationDiscoveryHypothesis |