Porovnat metody

	A/B test (online řízený experiment)	Adaptivní návrh klinického hodnocení	Sekvenční / skupinový sekvenční design studie
Obor	Plánování experimentů	Plánování experimentů	Plánování experimentů
Rodina	Hypothesis test	Hypothesis test	Hypothesis test
Rok vzniku	1935	1994	1979
Tvůrce	Ron Kohavi et al. (Microsoft); conceptual roots in R. A. Fisher's randomized experiments (1935)	Bauer & Köhne	O'Brien & Fleming; Pocock; Lan & DeMets
Typ	Parametric comparison (frequentist or Bayesian)	Adaptive hypothesis test with interim analyses	Adaptive stopping trial design
Původní zdroj	Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. ISBN: 9781108724265	Bauer, P. & Köhne, K. (1994). Evaluation of Experiments with Adaptive Interim Analyses. Biometrics, 50(4), 1029–1041.	O'Brien, P.C. & Fleming, T.R. (1979). A Multiple Testing Procedure for Clinical Trials. Biometrics, 35(3), 549–556.
Další názvy	split test, controlled experiment, two-variant test, A/B Testi (Online Kontrollü Deney)	adaptive design, group sequential design, sample size re-estimation, platform trial	group sequential design, adaptive stopping design, Ardışık Deneme Tasarımı (Sequential / Group Sequential)
Příbuzné	4	3	3
Shrnutí	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.	Adaptive clinical trial design is a flexible experimental framework, formalised by Bauer and Köhne in 1994, in which pre-specified rules allow the trial to be modified mid-course — adjusting sample size, treatment arms, or randomisation ratios — based on accumulating interim data while rigorously controlling the Type I error rate.	Sequential and group sequential trial designs allow a study to be stopped early — or continued — based on interim analyses conducted as data accumulate. The core framework was formalised by O'Brien and Fleming in 1979 and extended by Lan and DeMets's alpha-spending approach, and it controls the overall Type I error rate across all planned looks by pre-specifying both efficacy and futility boundaries before enrolment begins.