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Criterion
Decision Tree method illustration showing its working structure
Decision Making
Decision Tree
Paper illustration of an assumption matrix with prioritized test cards
Decision Making
Assumption Surfacing
Funnel Analysis workspace showing the question, observations, and next decision.
Growth
Funnel Analysis
A/B Testing workspace showing the question, observations, and next decision.
Growth
A/B Testing
Purposedifferent
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.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.When many visitors or users drop off along the way, the reason behind the number often stays hidden. Funnel Analysis exposes these transitions and makes visible exactly where the path collapses.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
MediumLowMediumHigh
Timedifferent
30-90 min45-90 min1-3 h1-4 Wochen
Participantsdifferent
1-62-81-51-6
Formatdifferent
Workshop + asyncWorkshop + asyncAsyncAsync
Outputdifferent
Decision Tree, Option Map, Assumption ListAssumption List, Critical Assumptions, Learning PlanFunnel report, Drop-off analysis, Optimization hypothesesExperiment results, Decision log, Learning summary
Tagsno overlap
DecisionTreeOptions
AssumptionsRiskDecisionDiscovery
AnalyticsConversionGrowth
ExperimentsGrowthAnalyticsValidation
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