Kendall Correlation
Kendall correlation is a non-parametric statistical measure that assesses the strength and direction of association between two ranked variables. It calculates the probability that the two variables are in the same order versus the probability they are in different orders, making it robust to outliers and suitable for ordinal data. Unlike Pearson correlation, it does not assume a linear relationship or normal distribution of the data.
Developers should learn Kendall correlation when working with data that is ordinal, has ties, or contains outliers, such as in ranking systems, survey responses, or non-normal datasets. It is particularly useful in machine learning for feature selection, evaluating model performance on ranked outputs, and in data analysis tasks where monotonic relationships need to be quantified without parametric assumptions.