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| Criterion |  Knowledge Modeling Shape-First Modeling |  Growth A/B Testing |  Product Discovery MVP Test Matrix |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
Purposedifferent | 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 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 several minimal variants compete to answer the same question, it makes comparing and choosing between them cleaner. It separates problem, assumption, solution, and evidence. The result is captured as a Test Matrix and a Test Plan. | 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 | Medium | High | Medium | Low |
Timedifferent | Halber Tag pro Domain Slice | 1-4 Wochen | 45-75 min | 30-60 min |
Participantsdifferent | 1-4 | 1-6 | 2-8 | 1-5 |
Formatdifferent | Workshop + async | Async | Workshop | Workshop + async |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Experiment results, Decision log, Learning summary | Test Matrix, Test Plan | Completed Experiment Canvas, Success Metric |
Tags1 shared | Knowledge graphValidationSemantic | ExperimentsGrowthAnalyticsValidation | ExperimentsValidationDiscoveryOptions | ExperimentsValidationDiscoveryHypothesis |