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| Criterion |  Product Strategy DIBB |  UX Research Affinity Diagramming |  Product Discovery Experiment Canvas |  Product Discovery Riskiest Assumption Test |
|---|---|---|---|---|
Purposedifferent | DIBB helps clarify target groups, value, goals, and priorities by making the logic behind a decision explicit. It captures results as a DIBB document, belief list, bet list, and learning report. | When research notes, feedback, or observations sit unconnected side by side, affinity diagramming sorts the raw material into solid themes. Many individual points turn into patterns that make decisions and opportunities clearer. | 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 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. |
Complexitydifferent | Low | Low | Low | Medium |
Timedifferent | 1-2 h | 45–90 min | 30-60 min | 1-2 Wochen pro Iteration |
Participantsdifferent | 2-8 | 3-10 | 1-5 | 2-6 |
Formatdifferent | Workshop + async | Workshop | Workshop + async | Workshop + async |
Outputdifferent | DIBB document, Belief list, Bet list, Learning report | Theme clusters, Insight statements, Opportunity areas | Completed Experiment Canvas, Success Metric | Prioritized Assumption List, Test Plan, Results Report |
Tagsno overlap | StrategyDecisionAssumptionsHypothesis | SynthesisQualitativeRoot cause | ExperimentsValidationDiscoveryHypothesis | ExperimentsValidationDiscoveryAssumptions |