方法对比

	Cohen's Kappa Coefficient	卡方独立性检验	麦克尼马尔检验
领域	统计学	统计学	统计学
方法族	Hypothesis test	Hypothesis test	Hypothesis test
起源年份	1960	1900	1947
提出者	Jacob Cohen	Karl Pearson	Quinn McNemar
类型	Inter-rater reliability coefficient	Nonparametric test of association	Nonparametric test for paired binary data
开创性文献	Cohen, J. (1960). A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement, 20(1), 37–46.	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.	McNemar, Q. (1947). Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika, 12(2), 153–157.
别名	kappa coefficient, kappa statistic, Cohen's Kappa (Değerlendiriciler Arası Uyum)	chi-squared test, Pearson's chi-square test, test of independence, ki-kare bağımsızlık testi	McNemar chi-square test, test for correlated proportions, paired binary test, McNemar Testi
相关	3	2	5
摘要	Cohen's kappa (κ) is a statistical measure of inter-rater reliability for categorical classifications, introduced by Jacob Cohen in 1960. Unlike simple percent agreement, kappa corrects for the level of agreement that would be expected purely by chance, making it the standard metric when two raters independently assign observations to the same set of mutually exclusive categories.	The chi-square test of independence is a nonparametric hypothesis test that examines whether two categorical variables are associated by comparing observed and expected frequencies in a cross-tabulation. It rests on the chi-square criterion introduced by Karl Pearson in 1900.	McNemar's test is a nonparametric hypothesis test that compares two paired (correlated) binary proportions, such as a yes/no measurement taken on the same subjects before and after an intervention. It was introduced by Quinn McNemar in 1947 and works on the 2×2 table of matched outcomes.