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Criterion
Paper illustration of Shape-first Modeling with its method-specific working model.
Knowledge Modeling
Shape-First Modeling
A/B Testing workspace showing the question, observations, and next decision.
Growth
A/B Testing
Paper illustration of Riskiest Assumption Test with a method-specific labelled workspace.
Product Discovery
Riskiest Assumption Test
Paper illustration of a calm four-quadrant Assumption Map with hypothesis cards and emphasis on important assumptions with little evidence.
Product Discovery
Assumption Mapping
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 an idea hinges on one critical unknown, it prioritizes testing that exact piece of uncertainty first. It separates problem, assumption, solution, and evidence. The result is captured as a prioritized list of assumptions, a test plan, and a result report.When assumptions still sit unordered in the room, it weighs uncertainty against leverage. It makes visible which hypotheses should be checked first.
Complexitydifferent
MediumHighMediumMedium
Timedifferent
Halber Tag pro Domain Slice1-4 Wochen1-2 Wochen pro Iteration45-60 min
Participantsdifferent
1-41-62-62-8
Formatdifferent
Workshop + asyncAsyncWorkshop + asyncWorkshop + async
Outputdifferent
SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape DocumentationExperiment results, Decision log, Learning summaryPrioritized Assumption List, Test Plan, Results ReportAssumption map, Test backlog, Risk ranking
Tags1 shared
Knowledge graphValidationSemantic
ExperimentsGrowthAnalyticsValidation
ExperimentsValidationDiscoveryAssumptions
AssumptionsRiskExperimentsValidation
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