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	Kendallovo Tau – korelačná koeficient poradia	Korelačný koeficient Pearsonovho momentového súčinu (r)	Spearmanov koeficient korelácie
Odbor	Štatistika	Štatistika	Štatistika
Rodina	Hypothesis test	Hypothesis test	Hypothesis test
Rok vzniku	1938	1895	1904
Tvorca	Maurice G. Kendall	Karl Pearson	Charles Spearman
Typ	Rank-based association measure	Parametric correlation	Nonparametric rank-based correlation
Pôvodný zdroj	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.	Spearman, C. (1904). The proof and measurement of association between two things. The American Journal of Psychology, 15, 72–101.
Ďalšie názvy	Kendall's tau, Kendall tau-b, tau correlation, Kendall Tau Korelasyonu	pearson r, product-moment correlation, bivariate correlation, Pearson Korelasyon Analizi	Spearman's rho, Spearman rank-order correlation, Spearman Sıra Korelasyonu
Príbuzné	4	4	4
Zhrnutie	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.	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.