方法对比

	拟合优度检验	赤池信息量准则 (AIC)
领域	模型评估	模型评估
方法族	MCDM	MCDM
起源年份	1900	1974
提出者	Karl Pearson	Hirotugu Akaike
类型	Hypothesis testing framework for model adequacy	Model selection metric
开创性文献	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.
别名	goodness of fit test, GOF test, model fit assessment	AIC
相关	4	4
摘要	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.