পদ্ধতির তুলনা করুন

	সুষমতা পরীক্ষা (Goodness-of-Fit Testing)	গড় বর্গ ত্রুটি (MSE)
ক্ষেত্র	মডেল মূল্যায়ন	মডেল মূল্যায়ন
পরিবার	MCDM	MCDM
উদ্ভবের বছর	1900	1809
প্রবর্তক	Karl Pearson	Carl Friedrich Gauss
ধরন	Hypothesis testing framework for model adequacy	Squared-error loss function
মৌলিক উৎস	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.	Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Hamburg: Perthes and Besser.
অপর নাম	goodness of fit test, GOF test, model fit assessment	MSE, L2 error, quadratic error
সম্পর্কিত	4	4
সারসংক্ষেপ	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.	Mean Squared Error is the foundational loss function for regression models, measuring the average squared deviation between predictions and observations. Originating from Gauss and Legendre's method of least squares (1805-1809), MSE is the basis for ordinary least squares regression and remains central to modern machine learning optimization.