This tutorial takes a look at how to describe relationships between variables using the correlation coefficient. Essentially there are three well-known correlation coefficients. Pearson’s correlation coefficient, r (or Pearson’s product-moment correlation coefficient to give it its full name), is a standardized measure of the strength of relationship between two variables. It can take any value from −1 (as one variable changes, the other changes in the opposite direction by the same amount), through 0 (as one variable changes the other doesn’t change at all), to +1 (as one variable changes, the other changes in the same direction by the same amount). Spearman’s correlation coefficient is Pearson’s correlation coefficient performed on data that have been converted into ranked scores. By analyzing ranks it has less-restrictive assumptions than Pearson’s r. Finally, Kendall’s tau is a non-parametric correlation coefficient similar to Spearman’s correlation coefficient, but is preferred for small data sets with a large number of tied ranks.