Posts about MAD

Nonparametric Cohen's d-consistent effect size

The effect size is a common way to describe a difference between two distributions. When these distributions are normal, one of the most popular approaches to express the effect size is Cohen's d. Unfortunately, it doesn't work great for non-normal distributions.

In this post, I will show a robust Cohen's d-consistent effect size formula for nonparametric distributions.

Read more

Yet another robust outlier detector

Outlier detection is an important step in data processing. Unfortunately, if the distribution is not normal (e.g., right-skewed and heavy-tailed), it's hard to choose a robust outlier detection algorithm that will not be affected by tricky distribution properties. During the last several years, I tried many different approaches, but I was not satisfied with their results. Finally, I found an algorithm to which I have (almost) no complaints. It's based on the double median absolute deviation and the Harrell-Davis quantile estimator. In this post, I will show how it works and why it's better than some other approaches.

Read more