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Criterion
Paper illustration of a branching category tree with a highlighted search path and possible dead ends.
UX Research
Tree Testing
A/B Testing workspace showing the question, observations, and next decision.
Growth
A/B Testing
Paper illustration showing three sorted card stacks and an open question about a possible group.
UX Research
Card Sorting
Paper illustration of Experiment Canvas with a method-specific labelled workspace.
Product Discovery
Experiment Canvas
Purposedifferent
When a navigation exists but search paths still fail, tree testing checks findability without visual distraction. The method shows whether labels, levels, and paths really lead to the intended destination.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 content only makes sense internally and users can't find the structure again, card sorting exposes their mental order. Terms, groups, and naming are then aligned with the target group's expectations.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
MediumHighLowLow
Timedifferent
1-2 Tage1-4 Wochen20-45 min30-60 min
Participantsdifferent
Based on research question1-6Based on research question1-5
Formatdifferent
AsyncAsyncWorkshop + asyncWorkshop + async
Outputdifferent
Findability Metrics, Path Analysis, Revised IAExperiment results, Decision log, Learning summaryContent groups, Label set, IA hypothesesCompleted Experiment Canvas, Success Metric
Tagsno overlap
Information architectureNavigationFindability
ExperimentsGrowthAnalyticsValidation
Information architectureNavigationStructure
ExperimentsValidationDiscoveryHypothesis
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