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Criterion
A/B Testing workspace showing the question, observations, and next decision.
Growth
A/B Testing
Paper illustration for PDCA Cycle.
Operations
PDCA Cycle
Design sheets and interface cards connect to jointly written pseudocode with highlighted unresolved questions.
Architecture
ARID (Active Reviews for Intermediate Designs)
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 improvement that still has to prove itself in everyday work, the method runs it through small learning loops. It connects planning, checking, and standardization into a repeatable learning mode.ARID examines how well developers can work with a technical design. It focuses on a part of the system that can still be revised, between the architecture overview and the detailed specification.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
HighLowMediumMedium
Timedifferent
1-4 Wochen1 h bis mehrere Wochen2–4 h review as a planning range; preparation and evaluation additional1-5 Tage
Participantsdifferent
1-61-8Designers, developers as users, and facilitationNutzertraffic
Formatdifferent
AsyncWorkshop + asyncWorkshop + asyncAsync
Outputdifferent
Experiment results, Decision log, Learning summaryPDCA Log, Experiment Plan, Learning Outcome, Standard ChangePrioritized development tasks, Jointly produced code or pseudocode, Observed difficulties and open design questions, Proposed design revisionsClick Data, Interest Signal, Learning Decision
Tagsno overlap
ExperimentsGrowthAnalyticsValidation
Continuous improvementLeanExperiments
ArchitectureEngineeringFeedbackValidationTasks
ValidationExperimentsDemandDiscovery
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