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| Criterion |  Product Discovery Hypothesis Prioritization Canvas |  Knowledge Modeling Competency Questions |  Product Discovery Fake Door Test |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
Purposedifferent | When many hypotheses compete for attention, it brings order to their learning sequence and importance. It separates problem, assumption, solution, and evidence. The result is captured as a prioritization canvas and a hypothesis backlog. | Competency Questions translate a domain model into concrete questions that it must be able to answer. The method keeps the model's scope clean and prevents pretty but useless structures. | 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. | 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 | Medium | Medium | Low |
Timedifferent | 60-90 min | 2-4 h | 1-5 Tage | 30-60 min |
Participantsdifferent | 3-8 | 2-8 | Nutzertraffic | 1-5 |
Formatdifferent | Workshop | Workshop + async | Async | Workshop + async |
Outputdifferent | Prioritization Canvas, Hypothesis Backlog | Competency Question Set, Required Concepts, Test Queries, Coverage Matrix | Click Data, Interest Signal, Learning Decision | Completed Experiment Canvas, Success Metric |
Tagsno overlap | ExperimentsPrioritizationDiscoveryHypothesis | OntologyKnowledge graphScopeRequirementsSemantic | ValidationExperimentsDemandDiscovery | ExperimentsValidationDiscoveryHypothesis |