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Criterion
Paper illustration of DIBB with its method-specific working model.
Product Strategy
DIBB
Decision Tree method illustration showing its working structure
Decision Making
Decision Tree
A/B Testing workspace showing the question, observations, and next decision.
Growth
A/B Testing
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.For decisions with follow-on paths and dependencies, a linear comparison is often not enough. A Decision Tree shows how options branch under conditions and which consequences hang on each branch.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.
Complexitydifferent
LowMediumHigh
Timedifferent
1-2 h30-90 min1-4 Wochen
Participantsdifferent
2-81-61-6
Formatdifferent
Workshop + asyncWorkshop + asyncAsync
Outputdifferent
DIBB document, Belief list, Bet list, Learning reportDecision Tree, Option Map, Assumption ListExperiment results, Decision log, Learning summary
Tagsno overlap
StrategyDecisionAssumptionsHypothesis
DecisionTreeOptions
ExperimentsGrowthAnalyticsValidation
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Methods with strong topical overlap with the current selection, not yet in the comparison.

Paper illustration of a calm four-quadrant Assumption Map with hypothesis cards and emphasis on important assumptions with little evidence.
Product Discovery
Assumption Mapping
Paper illustration of Experiment Canvas with a method-specific labelled workspace.
Product Discovery
Experiment Canvas
Paper illustration of MVP Test Matrix with a method-specific labelled workspace.
Product Discovery
MVP Test Matrix
Paper illustration of Riskiest Assumption Test with a method-specific labelled workspace.
Product Discovery
Riskiest Assumption Test