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| Criterion |  UX Research Surveys |  Decision Making Impact Filter |  Growth A/B Testing |
|---|---|---|---|
Purposedifferent | 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. | With new ideas or requests, the conversation often turns to solutions too early and to effect too little. An Impact Filter forces the initiative into an effect perspective and checks whether it creates a relevant difference. | 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 | Medium | Low | High |
Timedifferent | 3-14 Tage | 20-45 min | 1-4 Wochen |
Participantsdifferent | 50+ | 1-6 | 1-6 |
Formatdifferent | Async | Workshop + async | Async |
Outputdifferent | Survey Results, Charts, Segment Insights | Impact Filter, Success Criteria, Next Step | Experiment results, Decision log, Learning summary |
Tagsno overlap | QuantitativeResearchValidation | ImpactOutcomesFramingDecision | ExperimentsGrowthAnalyticsValidation |
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Methods with strong topical overlap with the current selection, not yet in the comparison.
