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Criterion
Paper illustration for Causal Factor Analysis.
Operations
Causal Factor Analysis
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
Paper illustration of Experiment Canvas with a method-specific labelled workspace.
Product Discovery
Experiment Canvas
Purposedifferent
For an event with a complicated course, the method breaks down the contributing factors along the timeline. It shows how conditions, decisions, and reactions together produce a course of events.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.When experiments become unreadable after the fact, it arranges hypothesis, signal, and learning goal on a single canvas. It separates problem, assumption, solution, and evidence. The result is captured as a Completed Experiment Canvas and a Success Metric.
Complexitydifferent
HighHighMediumLow
Timedifferent
2-6 h1-4 Wochen45-75 min30-60 min
Participantsdifferent
3-101-62-81-5
Formatdifferent
Workshop + asyncAsyncWorkshopWorkshop + async
Outputdifferent
Event Timeline, Causal Factor Chart, Cause List, Corrective ActionsExperiment results, Decision log, Learning summaryTest Matrix, Test PlanCompleted Experiment Canvas, Success Metric
Tagsno overlap
CausalityIncidentRoot causeTimeline
ExperimentsGrowthAnalyticsValidation
ExperimentsValidationDiscoveryOptions
ExperimentsValidationDiscoveryHypothesis
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