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| Criterion |  Product Discovery Experiment Canvas |  Decision Making Delphi Method |  Product Discovery Hypothesis Prioritization Canvas |  Product Strategy DIBB |
|---|---|---|---|---|
Purposedifferent | 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 knowledge is distributed and direct dominance should be avoided, groups quickly fall under the sway of their opinion leaders. The Delphi Method gathers assessments iteratively and gradually brings expert judgments closer together. | 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. | 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 | Low | High | Medium | Low |
Timedifferent | 30-60 min | 1-4 Wochen | 60-90 min | 1-2 h |
Participantsdifferent | 1-5 | 6-30 Experten | 3-8 | 2-8 |
Formatdifferent | Workshop + async | Async | Workshop | Workshop + async |
Outputdifferent | Completed Experiment Canvas, Success Metric | Expert Forecast, Consensus Range, Assumption Notes | Prioritization Canvas, Hypothesis Backlog | DIBB document, Belief list, Bet list, Learning report |
Tagsno overlap | ExperimentsValidationDiscoveryHypothesis | ForecastingExpertsDecisionStrategy | ExperimentsPrioritizationDiscoveryHypothesis | StrategyDecisionAssumptionsHypothesis |