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| Criterion |  UX Research Card Sorting |  Product Discovery Experiment Canvas |  Decision Making Assumption Surfacing |  Product Strategy DIBB |
|---|---|---|---|---|
Purposedifferent | When content only makes sense internally and users can't find the structure again, card sorting exposes their mental order. Terms, groups, and naming are then aligned with the target group's expectations. | 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. | Plans often contain assumptions that were never openly stated and remain dangerous precisely because of that. Assumption Surfacing makes these silent premises visible and prioritizes which of them carry the initiative or could sink it. | 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. |
Complexitysame | Low | Low | Low | Low |
Timedifferent | 20-45 min | 30-60 min | 45-90 min | 1-2 h |
Participantsdifferent | Based on research question | 1-5 | 2-8 | 2-8 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Content groups, Label set, IA hypotheses | Completed Experiment Canvas, Success Metric | Assumption List, Critical Assumptions, Learning Plan | DIBB document, Belief list, Bet list, Learning report |
Tagsno overlap | Information architectureNavigationStructure | ExperimentsValidationDiscoveryHypothesis | AssumptionsRiskDecisionDiscovery | StrategyDecisionAssumptionsHypothesis |