Myths About Linear and Monotonic Associations: Pearson’s <i>r</i>, Spearman’s <i>ρ</i>, and Kendall’s <i>τ</i>

Pearson’s correlation coefficient is considered a measure of linear association between bivariate random variables X and Y. It recommended not to use it for other forms associations. Indeed, nonlinear monotonic associations alternative measures like Spearman’s rank Kendall’s tau coefficients are more appropriate. These views or opinions on the estimation strongly rooted in statistical empirical sciences. After defining associations, we will demonstrate that these incorrect. should be ruled out priori measuring We provide examples practically relevant families distribution functions with which preferred over testing dependency Alternatively, family distributions Y correlation. Our show existing myths.