Spearman's rank correlation coefficient
![A Spearman correlation of 1 results when the two variables being compared are monotonically related, even if their relationship is not linear. This means that all data-points with greater x-values than that of a given data-point will have greater y-values as well. In contrast, this does not give a perfect Pearson correlation.](/Images/godic/202502/06/Spearman_fig1.svg3804.png")
![When the data are roughly elliptically distributed and there are no prominent outliers, the Spearman correlation and Pearson correlation give similar values.](/Images/godic/202502/06/Spearman_fig2.svg3804.png")
![The Spearman correlation is less sensitive than the Pearson correlation to strong outliers that are in the tails of both samples. That is because Spearman's rho limits the outlier to the value of its rank.](/Images/godic/202502/06/Spearman_fig3.svg3804.png")
![A positive Spearman correlation coefficient corresponds to an increasing monotonic trend between X and Y.](/Images/godic/202502/06/Spearman_fig5.svg3804.png")
In statistics, Spearman's rank correlation coefficient or Spearman's rho, named after Charles Spearman and often denoted by the Greek letter ![\rho](/Images/godic/202502/06/f7f177957cf064a93e9811df8fe65ed13804.png")
(rho) or as ![r_s](/Images/godic/202502/06/dc7b1297be65ac185205f73439f476703804.png")
, is a nonparametric measure of statistical dependence between two variables. It assesses how well the relationship between two variables can be described using a monotonic function. If there are no repeated data values, a perfect Spearman correlation of +1 or −1 occurs when each of the variables is a perfect monotone function of the other.