Comparar métodos

	Teste de Aderência	Critério de Informação de Akaike (AIC)
Área	Avaliação de modelos	Avaliação de modelos
Família	MCDM	MCDM
Ano de origem	1900	1974
Autor original	Karl Pearson	Hirotugu Akaike
Tipo	Hypothesis testing framework for model adequacy	Model selection metric
Fonte seminal	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.	Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723.
Outros nomes	goodness of fit test, GOF test, model fit assessment	AIC
Relacionados	4	4
Resumo	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 Akaike Information Criterion is an information-theoretic measure for model selection that balances goodness of fit against model complexity. Introduced by Hirotugu Akaike in 1974, AIC estimates the relative quality of models for a given dataset, penalizing additional parameters to prevent overfitting.