Сравнение методов

	Двойной слепой A/B-тест	A/B-тест (онлайн-контролируемый эксперимент)
Область	Планирование эксперимента	Планирование эксперимента
Семейство	Process / pipeline	Hypothesis test
Год появления	1935 (Fisher's formal randomized design); double-blinding in A/B testing: 1990s–2000s	1935
Автор метода	Evolved from clinical trial methodology; early systematic blinding attributed to James Lind (1753) and formalized by R. A. Fisher (1935)	Ron Kohavi et al. (Microsoft); conceptual roots in R. A. Fisher's randomized experiments (1935)
Тип	Randomized controlled experiment with blinding	Parametric comparison (frequentist or Bayesian)
Основополагающий источник	Schulz, K. F., Altman, D. G., & Moher, D. (2010). CONSORT 2010 Statement: Updated guidelines for reporting parallel group randomised trials. BMJ, 340, c332.	Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. ISBN: 9781108724265
Другие названия	double-blind split test, double-blinded A/B experiment, blinded two-arm randomized experiment, double-blind controlled A/B trial	split test, controlled experiment, two-variant test, A/B Testi (Online Kontrollü Deney)
Связанные	5	4
Сводка	A double-blind A/B test is a randomized experiment that compares two variants — a control (A) and a treatment (B) — while concealing group assignment from both participants and those administering or assessing the experiment. Combining the causal isolation of randomized assignment with blinding on both sides eliminates expectation-driven bias from participants and evaluator bias from analysts or administrators, producing cleaner causal estimates of treatment effect.	An A/B test is a randomized controlled experiment that simultaneously exposes two groups of users to a control variant (A) and a treatment variant (B) in order to determine whether a measured outcome differs significantly between them. The modern online controlled experiment framework was systematized by Ron Kohavi and colleagues at Microsoft in the early 2000s, building on R. A. Fisher's classical randomization principles from 1935. It is the dominant causal inference tool in web product development, digital marketing, and experimentation platforms.