방법 비교

	적응형 A/B 테스트	차단된 A/B 테스트
분야	실험설계	실험설계
계열	Process / pipeline	Process / pipeline
기원 연도	1952 (Robbins); applied to A/B testing from ~2010s onward	1926 (blocking principle); 2000s–2010s (online A/B testing application)
창시자	Herbert Robbins (bandit framework); Thompson Sampling formalized by William R. Thompson	R. A. Fisher (blocking principle); adapted to online A/B testing by industry practitioners
유형	Adaptive experimental design	Randomized controlled experiment with variance reduction
원전	Russo, D., Van Roy, B., Kazerouni, A., Osband, I., & Wen, Z. (2018). A Tutorial on Thompson Sampling. Foundations and Trends in Machine Learning, 11(1), 1–96.	Fisher, R. A. (1926). The arrangement of field experiments. Journal of the Ministry of Agriculture of Great Britain, 33, 503–513.
별칭	adaptive AB test, bandit A/B test, multi-armed bandit testing, online adaptive experiment	block-randomized A/B test, stratified A/B test, blocked split test, block-design A/B experiment
관련	6	4
요약	An Adaptive A/B test is an experimental design that dynamically reallocates traffic or participants toward better-performing variants during the experiment itself, rather than holding allocations fixed until the end. Drawing on multi-armed bandit algorithms such as Thompson Sampling or Upper Confidence Bound (UCB), it balances the exploration of uncertain variants with the exploitation of those already showing superior performance, typically yielding higher aggregate outcomes while still producing valid inferential conclusions.	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.