Comparer des méthodes

	Test A/B (Expérience Contrôlée en Ligne)	Test du Khi-deux d'indépendance
Domaine	Plans d'expériences	Statistique
Famille	Hypothesis test	Hypothesis test
Année d'origine	1935	1900
Auteur d'origine	Ron Kohavi et al. (Microsoft); conceptual roots in R. A. Fisher's randomized experiments (1935)	Karl Pearson
Type	Parametric comparison (frequentist or Bayesian)	Nonparametric test of association
Source fondatrice	Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. ISBN: 9781108724265	Pearson, K. (1900). On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philosophical Magazine, 50(302), 157–175.
Alias	split test, controlled experiment, two-variant test, A/B Testi (Online Kontrollü Deney)	chi-squared test, Pearson's chi-square test, test of independence, ki-kare bağımsızlık testi
Apparentées	4	2
Résumé	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 chi-square test of independence is a nonparametric hypothesis test that examines whether two categorical variables are associated by comparing observed and expected frequencies in a cross-tabulation. It rests on the chi-square criterion introduced by Karl Pearson in 1900.