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Criterion
A/B Testing workspace showing the question, observations, and next decision.
Growth
A/B Testing
Paper illustration of a branching category tree with a highlighted search path and possible dead ends.
UX Research
Tree Testing
Paper illustration showing three sorted card stacks and an open question about a possible group.
UX Research
Card Sorting
Paper illustration for Smoke Test.
Product Discovery
Smoke 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.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 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 demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all.
Complexitydifferent
HighMediumLowLow
Timedifferent
1-4 Wochen1-2 Tage20-45 min1-5 Tage
Participantsdifferent
1-6Based on research questionBased on research questionNutzertraffic
Formatdifferent
AsyncAsyncWorkshop + asyncAsync
Outputdifferent
Experiment results, Decision log, Learning summaryFindability Metrics, Path Analysis, Revised IAContent groups, Label set, IA hypothesesInterest Metrics, Conversion Signal, Learning Note
Tagsno overlap
ExperimentsGrowthAnalyticsValidation
Information architectureNavigationFindability
Information architectureNavigationStructure
ValidationExperimentsDemandGrowth
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