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Criterion
A/B Testing workspace showing the question, observations, and next decision.
Growth
A/B Testing
Paper illustration for Smoke Test.
Product Discovery
Smoke Test
Paper illustration of Cost of Delay with its method-specific working model.
Delivery
Cost of Delay
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.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.Helps clarify scope, order, and delivery flow in concrete terms. It makes work, constraints, and sequence manageable. The result is captured as a Cost of Delay table and prioritization sequence.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
HighLowHighMedium
Timedifferent
1-4 Wochen1-5 Tage90-180 min1-5 Tage
Participantsdifferent
1-6Nutzertraffic3-8Nutzertraffic
Formatdifferent
AsyncAsyncWorkshopAsync
Outputdifferent
Experiment results, Decision log, Learning summaryInterest Metrics, Conversion Signal, Learning NoteCoD Table, Prioritization SequenceClick Data, Interest Signal, Learning Decision
Tagsno overlap
ExperimentsGrowthAnalyticsValidation
ValidationExperimentsDemandGrowth
PrioritizationDeliveryEconomicsDecision
ValidationExperimentsDemandDiscovery
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