Σύγκριση μεθόδων

	Ανθεκτική συνάρτηση συσχέτισης Pearson	Συσχέτιση Βαθμίδων Kendall Tau	Συντελεστής Συσχέτισης Γραμμικής Συσχέτισης Pearson (r)
Πεδίο	Στατιστική	Στατιστική	Στατιστική
Οικογένεια	Hypothesis test	Hypothesis test	Hypothesis test
Έτος προέλευσης	1970s–1990s	1938	1895
Δημιουργός	Rand R. Wilcox and predecessors in robust statistics	Maurice G. Kendall	Karl Pearson
Τύπος	Robust bivariate association measure	Rank-based association measure	Parametric correlation
Θεμελιώδης πηγή	Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press. ISBN: 978-0123869838	Kendall, M. G. (1938). A new measure of rank correlation. Biometrika, 30(1–2), 81–93.	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.
Εναλλακτικές ονομασίες	winsorized correlation, percentage bend correlation, robust r, outlier-resistant correlation	Kendall's tau, Kendall tau-b, tau correlation, Kendall Tau Korelasyonu	pearson r, product-moment correlation, bivariate correlation, Pearson Korelasyon Analizi
Συναφείς	3	4	4
Σύνοψη	The robust Pearson correlation is an outlier-resistant measure of linear association between two continuous variables. By applying Winsorizing, trimming, or percentage-bend transformations before computing the classic Pearson r, it retains the interpretability of a correlation coefficient while dramatically reducing the distortion caused by extreme values.	Kendall Tau is a nonparametric rank correlation coefficient introduced by Maurice G. Kendall in 1938 to measure the strength and direction of a monotone association between two ordinal or continuous variables. It is particularly suited to small samples and datasets containing many tied ranks, where the Spearman coefficient can be less stable.	The Pearson product-moment correlation coefficient (r) is a parametric measure of the direction and strength of the linear association between two continuous variables. Introduced by Karl Pearson in 1895, it remains the most widely used bivariate correlation statistic in the social, health, and natural sciences. The coefficient ranges from −1 (perfect negative linear relationship) to +1 (perfect positive), with 0 indicating no linear association.