Сравнение на методи

	Клъстерно рандомизиран A/B тест	Блокиран A/B тест
Област	Планиране на експеримента	Планиране на експеримента
Семейство	Process / pipeline	Process / pipeline
Година на възникване	2010s (digital platforms); cluster RCT roots date to the 1970s–1980s	1926 (blocking principle); 2000s–2010s (online A/B testing application)
Създател	Developed from cluster randomized trial methodology; popularized in digital experimentation by researchers at Facebook, LinkedIn, and Microsoft Research (2010s)	R. A. Fisher (blocking principle); adapted to online A/B testing by industry practitioners
Тип	Experimental design	Randomized controlled experiment with variance reduction
Основополагащ източник	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.	Fisher, R. A. (1926). The arrangement of field experiments. Journal of the Ministry of Agriculture of Great Britain, 33, 503–513.
Други названия	cluster A/B test, group-randomized A/B test, network A/B test, cluster-level split test	block-randomized A/B test, stratified A/B test, blocked split test, block-design A/B experiment
Свързани	6	4
Резюме	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 blocked A/B test is an experimental design that partitions units (users, subjects, or clusters) into homogeneous blocks before randomly assigning them to treatment A or treatment B within each block. Blocking reduces within-experiment noise by ensuring that known sources of variation — such as device type, geography, or user tenure — are balanced across conditions, yielding more precise estimates of the treatment effect than a simple unblocked A/B test.