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Criterion
A/B Testing workspace showing the question, observations, and next decision.
Growth
A/B Testing
Paper illustration for Causal Factor Analysis.
Operations
Causal Factor Analysis
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 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 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
HighHighMedium
Timedifferent
1-4 Wochen2-6 h1-5 Tage
Participantsdifferent
1-63-10Nutzertraffic
Formatdifferent
AsyncWorkshop + asyncAsync
Outputdifferent
Experiment results, Decision log, Learning summaryEvent Timeline, Causal Factor Chart, Cause List, Corrective ActionsClick Data, Interest Signal, Learning Decision
Tagsno overlap
ExperimentsGrowthAnalyticsValidation
CausalityIncidentRoot causeTimeline
ValidationExperimentsDemandDiscovery
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Often compared together

Methods with strong topical overlap with the current selection, not yet in the comparison.

Paper illustration for Smoke Test.
Product Discovery
Smoke Test
Paper illustration of Experiment Canvas with a method-specific labelled workspace.
Product Discovery
Experiment Canvas
Paper illustration of a Learning Card with four fields for hypothesis, observation, insight, and action.
Product Discovery
Learning Card
Paper illustration of MVP Test Matrix with a method-specific labelled workspace.
Product Discovery
MVP Test Matrix