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Criterion
Paper illustration for PERT Estimation.
Decision Making
PERT Estimation
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
Paper illustration of MVP Test Matrix with a method-specific labelled workspace.
Product Discovery
MVP Test Matrix
Purposedifferent
On complex initiatives, a plain average tends to understate just how uncertain the outcome really is. It separates options, evaluation criteria, and open risks. The result is captured as a PERT Estimate, Expected Value, and Risk Notes.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.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
MediumMediumHighMedium
Timedifferent
15-45 min30-90 min1-4 Wochen45-75 min
Participantsdifferent
1-81-61-62-8
Formatdifferent
Workshop + asyncWorkshop + asyncAsyncWorkshop
Outputdifferent
PERT Estimate, Expected Value, Risk NotesDecision Tree, Option Map, Assumption ListExperiment results, Decision log, Learning summaryTest Matrix, Test Plan
Tagsno overlap
EstimationUncertaintyRisk
DecisionTreeOptions
ExperimentsGrowthAnalyticsValidation
ExperimentsValidationDiscoveryOptions
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