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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
Surveys method illustration showing its working structure
UX Research
Surveys
Paper illustration showing three sorted card stacks and an open question about a possible group.
UX Research
Card Sorting
A/B Testing workspace showing the question, observations, and next decision.
Growth
A/B Testing
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 a topic needs to be validated broadly and many people can answer the same question, surveys gather structured feedback in a scalable form. Answers become comparable and segmentable instead of remaining merely anecdotal.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 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.
Complexitydifferent
MediumMediumLowHigh
Timedifferent
1-2 Tage3-14 Tage20-45 min1-4 Wochen
Participantsdifferent
Based on research question50+Based on research question1-6
Formatdifferent
AsyncAsyncWorkshop + asyncAsync
Outputdifferent
Findability Metrics, Path Analysis, Revised IASurvey Results, Charts, Segment InsightsContent groups, Label set, IA hypothesesExperiment results, Decision log, Learning summary
Tagsno overlap
Information architectureNavigationFindability
QuantitativeResearchValidation
Information architectureNavigationStructure
ExperimentsGrowthAnalyticsValidation
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