পদ্ধতির তুলনা করুন

	এ/বি পরীক্ষা (অনলাইন নিয়ন্ত্রিত পরীক্ষা)	মাল্টি-আর্মড ব্যান্ডিট (ইউসিবি, থম্পসন স্যাম্পলিং)
ক্ষেত্র	পরীক্ষামূলক নকশা	পরীক্ষামূলক নকশা
পরিবার	Hypothesis test	Hypothesis test
উদ্ভবের বছর	1935	1952
প্রবর্তক	Ron Kohavi et al. (Microsoft); conceptual roots in R. A. Fisher's randomized experiments (1935)	Robbins (1952); UCB1 by Auer et al. (2002); Thompson sampling by Thompson (1933)
ধরন	Parametric comparison (frequentist or Bayesian)	Sequential decision / bandit algorithm
মৌলিক উৎস	Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. ISBN: 9781108724265	Auer, P., Cesa-Bianchi, N., & Fischer, P. (2002). Finite-Time Analysis of the Multiarmed Bandit Problem. Machine Learning, 47(2–3), 235–256.
অপর নাম	split test, controlled experiment, two-variant test, A/B Testi (Online Kontrollü Deney)	MAB, bandit algorithm, UCB1, Thompson sampling
সম্পর্কিত	4	4
সারসংক্ষেপ	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.	The multi-armed bandit (MAB) is an adaptive experimental framework that allocates trials sequentially across competing arms to minimise cumulative regret while simultaneously learning which arm performs best. Formalised by Robbins in 1952 and given finite-time guarantees by Auer et al. (2002), it balances exploration of uncertain options against exploitation of currently known best options — outperforming classical A/B testing whenever early stopping or cost-sensitive allocation matters.