Vertaile menetelmiä

	Adaptiivinen A/B-testaus – Adaptiivinen A/B-testi	Monikäsivälineinen koe
Tieteenala	Koesuunnittelu	Koesuunnittelu
Menetelmäperhe	Process / pipeline	Process / pipeline
Syntyvuosi	1952 (Robbins); applied to A/B testing from ~2010s onward	1990s–2000s (clinical formalization); multi-arm concept implicit in ANOVA-era factorial designs
Kehittäjä	Herbert Robbins (bandit framework); Thompson Sampling formalized by William R. Thompson	Developed within clinical trials methodology; formalized by Parmar, Royston and colleagues (UK MRC CTU, early 2000s)
Tyyppi	Adaptive experimental design	Experimental design
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.	Royston, P., Parmar, M. K. B., & Qian, W. (2003). Novel designs for multi-arm clinical trials with survival outcomes with an application in ovarian cancer. Statistics in Medicine, 22(14), 2239–2256.
Rinnakkaisnimet	adaptive AB test, bandit A/B test, multi-armed bandit testing, online adaptive experiment	multi-arm trial, multiple-arm experiment, multi-group experiment, many-arm design
Liittyvät	6	5
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 multi-arm experiment simultaneously compares three or more treatment or intervention conditions — each called an arm — against a shared control or against one another. By testing multiple alternatives in a single study, it yields more information per participant than running separate two-group experiments sequentially, while controlling the overall Type I error rate through pre-specified comparison strategies.