Krahasoni metodat

	Testet e përshtatshmërisë	Kriteri i Informacionit Akaike (AIC)
Fusha	Vlerësimi i modeleve	Vlerësimi i modeleve
Familja	MCDM	MCDM
Viti i origjinës	1900	1974
Krijuesi	Karl Pearson	Hirotugu Akaike
Lloji	Hypothesis testing framework for model adequacy	Model selection metric
Burimi themelues	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.
Emërtime të tjera	goodness of fit test, GOF test, model fit assessment	AIC
Të lidhura	4	4
Përmbledhja	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.