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| Criterion |  Product Discovery Hypothesis Prioritization Canvas |  Decision Making OODA Loop |  Product Discovery Fake Door Test |  Product Strategy DIBB |
|---|---|---|---|---|
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. | In dynamic situations, decisions become outdated faster than they can be prepared. The OODA Loop holds observing, orienting, deciding, and acting together as a recurring rhythm so reaction does not slide into inertia. | 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. | 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. |
Complexitydifferent | Medium | Medium | Medium | Low |
Timedifferent | 60-90 min | 15-60 min je Zyklus | 1-5 Tage | 1-2 h |
Participantsdifferent | 3-8 | 1-8 | Nutzertraffic | 2-8 |
Formatdifferent | Workshop | Workshop + async | Async | Workshop + async |
Outputdifferent | Prioritization Canvas, Hypothesis Backlog | Situation Assessment, Decision Loop, Action Updates | Click Data, Interest Signal, Learning Decision | DIBB document, Belief list, Bet list, Learning report |
Tagsno overlap | ExperimentsPrioritizationDiscoveryHypothesis | DecisionChangeLearningStrategy | ValidationExperimentsDemandDiscovery | StrategyDecisionAssumptionsHypothesis |