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Criterion
A/B Testing workspace showing the question, observations, and next decision.
Growth
A/B Testing
Paper illustration for Kaizen Event.
Operations
Kaizen Event
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 a tightly scoped process segment with noticeable waste, the method bundles shared energy for change. It suits situations that call for fast learning loops and visible adjustments.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 Wochen0.5-5 Tage30-60 min1-5 Tage
Participantsdifferent
1-64-101-5Nutzertraffic
Formatdifferent
AsyncWorkshopWorkshop + asyncAsync
Outputdifferent
Experiment results, Decision log, Learning summaryKaizen Charter, Waste List, Improvement Experiments, Standard Work UpdateCompleted Experiment Canvas, Success MetricClick Data, Interest Signal, Learning Decision
Tagsno overlap
ExperimentsGrowthAnalyticsValidation
LeanContinuous improvementOperations
ExperimentsValidationDiscoveryHypothesis
ValidationExperimentsDemandDiscovery
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