Salīdzināt metodes

	Robustā Pīrsona korelācija	Pīrsona momentu korelācijas koeficients	Spīrmena ranga sakarības koeficients
Nozare	Statistika	Statistika	Statistika
Saime	Hypothesis test	Hypothesis test	Hypothesis test
Izcelsmes gads	1970s–1990s	1895	1904
Autors	Rand R. Wilcox and predecessors in robust statistics	Karl Pearson	Charles Spearman
Tips	Robust bivariate association measure	Parametric correlation	Nonparametric rank-based correlation
Pirmavots	Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press. ISBN: 978-0123869838	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.
Citi nosaukumi	winsorized correlation, percentage bend correlation, robust r, outlier-resistant correlation	pearson r, product-moment correlation, bivariate correlation, Pearson Korelasyon Analizi	Spearman's rho, Spearman rank-order correlation, Spearman Sıra Korelasyonu
Saistītās	3	4	4
Kopsavilkums	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.	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.