Comparer des méthodes

	Test d'adéquation	Critère d'information bayésien (BIC)
Domaine	Évaluation de modèles	Évaluation de modèles
Famille	MCDM	MCDM
Année d'origine	1900	1978
Auteur d'origine	Karl Pearson	Gideon E. Schwarz
Type	Hypothesis testing framework for model adequacy	Bayesian model selection metric
Source fondatrice	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.	Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461-464.
Alias	goodness of fit test, GOF test, model fit assessment	BIC, Schwarz criterion, Schwarz information criterion
Apparentées	4	4
Résumé	Goodness-of-fit (GOF) testing is a framework for assessing whether observed data are consistent with a hypothesized probability distribution or model. Originating from Karl Pearson's chi-square test (1900), GOF tests quantify the discrepancy between data and model predictions, yielding p-values to judge whether observed deviations are statistically significant or due to random chance.	The Bayesian Information Criterion is an information-theoretic model selection criterion that approximates Bayesian model comparison. Introduced by Gideon Schwarz in 1978, BIC penalizes model complexity more heavily than AIC by using a sample-size-dependent penalty, making it particularly suitable for identifying the true underlying model structure.