ScholarGate
어시스턴트

방법 비교

선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.

군집 무작위 A/B 테스트×차단된 A/B 테스트×
분야실험설계실험설계
계열Process / pipelineProcess / pipeline
기원 연도2010s (digital platforms); cluster RCT roots date to the 1970s–1980s1926 (blocking principle); 2000s–2010s (online A/B testing application)
창시자Developed from cluster randomized trial methodology; popularized in digital experimentation by researchers at Facebook, LinkedIn, and Microsoft Research (2010s)R. A. Fisher (blocking principle); adapted to online A/B testing by industry practitioners
유형Experimental designRandomized controlled experiment with variance reduction
원전Ugander, J., Karrer, B., Backstrom, L., & Kleinberg, J. (2013). Graph cluster randomization: Network exposure to multiple universes. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 329–337. DOI ↗Fisher, R. A. (1926). The arrangement of field experiments. Journal of the Ministry of Agriculture of Great Britain, 33, 503–513. link ↗
별칭cluster A/B test, group-randomized A/B test, network A/B test, cluster-level split testblock-randomized A/B test, stratified A/B test, blocked split test, block-design A/B experiment
관련64
요약A cluster randomized A/B test is an experimental design in which intact groups (clusters) — such as cities, schools, social network communities, or app user segments — are randomly assigned as whole units to either the treatment (A) or control (B) condition, rather than randomizing individual users or subjects. This approach is used when treatment effects would spill over between individuals if individual-level randomization were applied, or when the intervention must be delivered at the group level.A blocked A/B test is an experimental design that partitions units (users, subjects, or clusters) into homogeneous blocks before randomly assigning them to treatment A or treatment B within each block. Blocking reduces within-experiment noise by ensuring that known sources of variation — such as device type, geography, or user tenure — are balanced across conditions, yielding more precise estimates of the treatment effect than a simple unblocked A/B test.
ScholarGate데이터셋
  1. v1
  2. 2 출처
  3. PUBLISHED
  1. v1
  2. 2 출처
  3. PUBLISHED

검색으로 이동 슬라이드 다운로드

ScholarGate방법 비교: Cluster Randomized A/B Test · Blocked A/B Test. 2026-06-17에 다음에서 검색함: https://scholargate.app/ko/compare