What's the difference between Spearman and Pearson correlation?
Also asked, what is the difference between Pearson Spearman and Kendall correlation?
It means that Kendall correlation is preferred when there are small samples or some outliers. Kendall correlation has a O(n^2) computation complexity comparing with O(n logn) of Spearman correlation, where n is the sample size. Spearman's rho usually is larger than Kendall's tau.
Herein, why would you use Spearman's rank?
Spearman's Rank correlation coefficient is a technique which can be used to summarise the strength and direction (negative or positive) of a relationship between two variables. The result will always be between 1 and minus 1. Create a table from your data. Rank the two data sets.
The Spearman correlation coefficient, rs, can take values from +1 to -1. A rs of +1 indicates a perfect association of ranks, a rs of zero indicates no association between ranks and a rs of -1 indicates a perfect negative association of ranks. The closer rs is to zero, the weaker the association between the ranks.