Linganisha mbinu

	Jaribio la A/B (Jaribio Lililodhibitiwa Mtandaoni)	Muundo Unaobadilika wa Jaribio la Kliniki
Nyanja	Muundo wa Majaribio	Muundo wa Majaribio
Familia	Hypothesis test	Hypothesis test
Mwaka wa asili	1935	1994
Mwanzilishi	Ron Kohavi et al. (Microsoft); conceptual roots in R. A. Fisher's randomized experiments (1935)	Bauer & Köhne
Aina	Parametric comparison (frequentist or Bayesian)	Adaptive hypothesis test with interim analyses
Chanzo asilia	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.
Majina mbadala	split test, controlled experiment, two-variant test, A/B Testi (Online Kontrollü Deney)	adaptive design, group sequential design, sample size re-estimation, platform trial
Zinazohusiana	4	3
Muhtasari	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.