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Criterion
A/B Testing workspace showing the question, observations, and next decision.
Growth
A/B Testing
Paper illustration of Lean Startup with its method-specific working model.
Innovation
Lean Startup
Paper illustration of Experiment Canvas with a method-specific labelled workspace.
Product Discovery
Experiment Canvas
Paper illustration for Fake Door Test
Product Discovery
Fake Door Test
Purposedifferent
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.For uncertain business assumptions, the method forces the idea into contact with real market reactions early. It separates wishful picture, assumption, and observable behavior, so that learning becomes faster than planning. This translates uncertainty into measurable insight.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.When demand is still unclear, it measures interest in a feature before it is built. It separates real behavior from polite agreement and deliberately keeps build effort small.
Complexitydifferent
HighMediumLowMedium
Timedifferent
1-4 WochenWochen bis Monate je Lernzyklus30-60 min1-5 Tage
Participantsdifferent
1-62-81-5Nutzertraffic
Formatdifferent
AsyncWorkshop + asyncWorkshop + asyncAsync
Outputdifferent
Experiment results, Decision log, Learning summaryHypothesis list, MVPs, Learning reports, Pivot or persevere decisionCompleted Experiment Canvas, Success MetricClick Data, Interest Signal, Learning Decision
Tags1 shared
ExperimentsGrowthAnalyticsValidation
LeanStartupValidationMVP
ExperimentsValidationDiscoveryHypothesis
ValidationExperimentsDemandDiscovery
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