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Criterion
Anonymous individual judgments pass through feedback loops to form a distribution.
Decision Making
Delphi Method
A/B Testing workspace showing the question, observations, and next decision.
Growth
A/B Testing
Paper illustration of Riskiest Assumption Test with a method-specific labelled workspace.
Product Discovery
Riskiest Assumption Test
Paper illustration of DIBB with its method-specific working model.
Product Strategy
DIBB
Purposedifferent
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 two variants compete, discussions quickly decide by taste rather than effect. A/B Testing checks behavior under controlled conditions and separates real improvement from chance or expectation effects.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.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
HighHighMediumLow
Timedifferent
1-4 Wochen1-4 Wochen1-2 Wochen pro Iteration1-2 h
Participantsdifferent
6-30 Experten1-62-62-8
Formatdifferent
AsyncAsyncWorkshop + asyncWorkshop + async
Outputdifferent
Expert Forecast, Consensus Range, Assumption NotesExperiment results, Decision log, Learning summaryPrioritized Assumption List, Test Plan, Results ReportDIBB document, Belief list, Bet list, Learning report
Tagsno overlap
ForecastingExpertsDecisionStrategy
ExperimentsGrowthAnalyticsValidation
ExperimentsValidationDiscoveryAssumptions
StrategyDecisionAssumptionsHypothesis
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