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Criterion
Decision Tree method illustration showing its working structure
Decision Making
Decision Tree
Paper illustration for Smoke Test.
Product Discovery
Smoke Test
A/B Testing workspace showing the question, observations, and next decision.
Growth
A/B Testing
Paper illustration of MVP Test Matrix with a method-specific labelled workspace.
Product Discovery
MVP Test Matrix
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.When demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all.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 several minimal variants compete to answer the same question, it makes comparing and choosing between them cleaner. It separates problem, assumption, solution, and evidence. The result is captured as a Test Matrix and a Test Plan.
Complexitydifferent
MediumLowHighMedium
Timedifferent
30-90 min1-5 Tage1-4 Wochen45-75 min
Participantsdifferent
1-6Nutzertraffic1-62-8
Formatdifferent
Workshop + asyncAsyncAsyncWorkshop
Outputdifferent
Decision Tree, Option Map, Assumption ListInterest Metrics, Conversion Signal, Learning NoteExperiment results, Decision log, Learning summaryTest Matrix, Test Plan
Tagsno overlap
DecisionTreeOptions
ValidationExperimentsDemandGrowth
ExperimentsGrowthAnalyticsValidation
ExperimentsValidationDiscoveryOptions
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