方法对比

	自适应A/B测试	阻塞 A/B 测试
领域	实验设计	实验设计
方法族	Process / pipeline	Process / pipeline
起源年份	1952 (Robbins); applied to A/B testing from ~2010s onward	1926 (blocking principle); 2000s–2010s (online A/B testing application)
提出者	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
类型	Adaptive experimental design	Randomized controlled experiment with variance reduction
开创性文献	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.
别名	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
相关	6	4
摘要	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.