Comparer des méthodes

	Test A/B adaptatif	Expérience à bras multiples
Domaine	Plans d'expériences	Plans d'expériences
Famille	Process / pipeline	Process / pipeline
Année d'origine	1952 (Robbins); applied to A/B testing from ~2010s onward	1990s–2000s (clinical formalization); multi-arm concept implicit in ANOVA-era factorial designs
Auteur d'origine	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)
Type	Adaptive experimental design	Experimental design
Source fondatrice	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.
Alias	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
Apparentées	6	5
Résumé	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.