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Criterion
A/B Testing workspace showing the question, observations, and next decision.
Growth
A/B Testing
Paper illustration of RAID Log with a method-specific labelled workspace.
Delivery
RAID Log
Paper illustration for Smoke Test.
Product Discovery
Smoke Test
Paper illustration of Experiment Canvas with a method-specific labelled workspace.
Product Discovery
Experiment Canvas
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.Helps clarify scope, sequence, and delivery flow in concrete terms. It makes work, boundaries, and order steerable. The result is captured as a RAID Log and a source for status reporting.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.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
HighLowLowLow
Timedifferent
1-4 Wochen30 min Setup, dann laufend1-5 Tage30-60 min
Participantsdifferent
1-61-3 maintaining, briefing for everyoneNutzertraffic1-5
Formatdifferent
AsyncAsyncAsyncWorkshop + async
Outputdifferent
Experiment results, Decision log, Learning summaryRAID Log, Status Report SourceInterest Metrics, Conversion Signal, Learning NoteCompleted Experiment Canvas, Success Metric
Tagsno overlap
ExperimentsGrowthAnalyticsValidation
RiskTrackingStakeholdersGovernance
ValidationExperimentsDemandGrowth
ExperimentsValidationDiscoveryHypothesis
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