Порівняння методів

	Кластерний рандомізований A/B-тест	Експеримент з багатьма рукавами
Галузь	Планування експерименту	Планування експерименту
Родина	Process / pipeline	Process / pipeline
Рік появи	2010s (digital platforms); cluster RCT roots date to the 1970s–1980s	1990s–2000s (clinical formalization); multi-arm concept implicit in ANOVA-era factorial designs
Автор методу	Developed from cluster randomized trial methodology; popularized in digital experimentation by researchers at Facebook, LinkedIn, and Microsoft Research (2010s)	Developed within clinical trials methodology; formalized by Parmar, Royston and colleagues (UK MRC CTU, early 2000s)
Тип	Experimental design	Experimental design
Основоположне джерело	Ugander, J., Karrer, B., Backstrom, L., & Kleinberg, J. (2013). Graph cluster randomization: Network exposure to multiple universes. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 329–337.	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.
Інші назви	cluster A/B test, group-randomized A/B test, network A/B test, cluster-level split test	multi-arm trial, multiple-arm experiment, multi-group experiment, many-arm design
Пов'язані	6	5
Підсумок	A cluster randomized A/B test is an experimental design in which intact groups (clusters) — such as cities, schools, social network communities, or app user segments — are randomly assigned as whole units to either the treatment (A) or control (B) condition, rather than randomizing individual users or subjects. This approach is used when treatment effects would spill over between individuals if individual-level randomization were applied, or when the intervention must be delivered at the group level.	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.