Spearman Correlation Coefficient for When Pearson Isn’t Enough

The Spearman correlation coefficient is a statistical measure for assessing monotonic relationships between variables, which is particularly useful when the relationship is non-linear. Unlike the Pearson correlation that requires a linear association, Spearman's method first converts data values into ranks. Using a fish market dataset, it is shown how Pearson correlation can underestimate a strong non-linear relationship, whereas Spearman's rank-based approach provides a more accurate measure of the association's strength. The calculation involves assigning ranks to each variable's data points, handling ties by using average ranks, and then applying the Pearson correlation formula to these ranks.