Comparer des méthodes

	Test A/B en simple aveugle	Expérience à bras multiples
Domaine	Plans d'expériences	Plans d'expériences
Famille	Process / pipeline	Process / pipeline
Année d'origine	mid-20th century (blinded RCT framework); A/B test nomenclature ~1990s–2000s	1990s–2000s (clinical formalization); multi-arm concept implicit in ANOVA-era factorial designs
Auteur d'origine	Fisher, R. A. (randomisation basis); blinding practice formalised in clinical trials mid-20th century	Developed within clinical trials methodology; formalized by Parmar, Royston and colleagues (UK MRC CTU, early 2000s)
Type	Controlled experiment with partial blinding	Experimental design
Source fondatrice	Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. ISBN: 978-1108724265	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	single-masked A/B test, single-blind split test, blinded two-condition experiment, participant-blind A/B test	multi-arm trial, multiple-arm experiment, multi-group experiment, many-arm design
Apparentées	6	5
Résumé	A single-blind A/B test is a controlled two-condition experiment in which participants are randomised to condition A (control) or condition B (treatment) but are kept unaware of which condition they have received, while researchers and analysts remain aware. The blind prevents participants from changing their behaviour in response to knowledge of their assignment, reducing demand characteristics and response bias while still allowing the investigator to monitor the trial.	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.