Comparar métodos

	Prueba A/B (Experimento Controlado en Línea)	Bandido Multi-Brazo (UCB, Muestreo de Thompson)
Campo	Diseño experimental	Diseño experimental
Familia	Hypothesis test	Hypothesis test
Año de origen	1935	1952
Autor original	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)
Tipo	Parametric comparison (frequentist or Bayesian)	Sequential decision / bandit algorithm
Fuente seminal	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.
Alias	split test, controlled experiment, two-variant test, A/B Testi (Online Kontrollü Deney)	MAB, bandit algorithm, UCB1, Thompson sampling
Relacionados	4	4
Resumen	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.