Сравнение методов

	Кластерный рандомизированный A/B-тест	Полевой эксперимент
Область	Планирование эксперимента	Планирование эксперимента
Семейство	Process / pipeline	Process / pipeline
Год появления	2010s (digital platforms); cluster RCT roots date to the 1970s–1980s	1920s–1930s (agriculture); 1990s–2000s (social sciences)
Автор метода	Developed from cluster randomized trial methodology; popularized in digital experimentation by researchers at Facebook, LinkedIn, and Microsoft Research (2010s)	Formalized by R. A. Fisher (1935); systematized in social sciences by Harrison & List (2004)
Тип	Experimental design	Experimental design
Основополагающий источник	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.	Harrison, G. W., & List, J. A. (2004). Field experiments. Journal of Economic Literature, 42(4), 1009–1055.
Другие названия	cluster A/B test, group-randomized A/B test, network A/B test, cluster-level split test	field trial, natural field experiment, randomized field experiment, field RCT
Связанные	6	5
Сводка	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 field experiment applies the logic of a randomized controlled trial in a naturally occurring, real-world environment rather than an artificial laboratory. Participants are randomly assigned to treatment and control conditions while going about everyday activities, allowing researchers to estimate causal effects with high internal validity while preserving a level of ecological realism that laboratory settings cannot offer. The design is especially prominent in economics, public health, political science, and development research.