Vertaile menetelmiä

	Adaptiivinen A/B-testaus – Adaptiivinen A/B-testi	Estetty A/B-testi
Tieteenala	Koesuunnittelu	Koesuunnittelu
Menetelmäperhe	Process / pipeline	Process / pipeline
Syntyvuosi	1952 (Robbins); applied to A/B testing from ~2010s onward	1926 (blocking principle); 2000s–2010s (online A/B testing application)
Kehittäjä	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
Tyyppi	Adaptive experimental design	Randomized controlled experiment with variance reduction
Alkuperäislähde	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.
Rinnakkaisnimet	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
Liittyvät	6	4
Tiivistelmä	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.