Compară metode

	Corelația Pearson robustă	Corelația rangurilor Tau Kendall	Coeficientul de corelație a rangurilor Spearman
Domeniu	Statistică	Statistică	Statistică
Familie	Hypothesis test	Hypothesis test	Hypothesis test
Anul apariției	1970s–1990s	1938	1904
Autorul original	Rand R. Wilcox and predecessors in robust statistics	Maurice G. Kendall	Charles Spearman
Tip	Robust bivariate association measure	Rank-based association measure	Nonparametric rank-based correlation
Sursa seminală	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.	Spearman, C. (1904). The proof and measurement of association between two things. The American Journal of Psychology, 15, 72–101.
Denumiri alternative	winsorized correlation, percentage bend correlation, robust r, outlier-resistant correlation	Kendall's tau, Kendall tau-b, tau correlation, Kendall Tau Korelasyonu	Spearman's rho, Spearman rank-order correlation, Spearman Sıra Korelasyonu
Înrudite	3	4	4
Rezumat	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 Spearman rank correlation coefficient (ρ) is a nonparametric measure of the monotonic association between two variables. Introduced by Charles Spearman in 1904, it converts raw observations to ranks and measures how consistently one variable increases as the other increases, without assuming a normal distribution or a linear relationship.