# Nonparametric effect size: Cohen's d vs. Glass's delta

by Andrey Akinshin · 2023-01-24

In the previous posts, I discussed the idea of nonparametric effect size measures consistent with Cohen’s d under normality. However, Cohen’s d is not always the best effect size measure, even in the normal case.

In this post, we briefly discuss a case study in which a nonparametric version of Glass’s delta is preferable than the previously suggested Cohen’s d-consistent measure.

## Nonparametric versions of Cohen’s d and Glass’s delta

In the scope of this post, we use the following nonparametric modifications of Cohen’s d and Glass’s delta:

$$d(x, y) = \frac{\operatorname{median}(y) - \operatorname{median}(x)}{\operatorname{PMAD}(x, y)},$$ $$\Delta(x, y) = \frac{\operatorname{median}(y) - \operatorname{median}(x)}{\operatorname{MAD}(x)},$$

where $\operatorname{MAD}$ is the median absolute deviation:

$$\operatorname{MAD}(x) = \operatorname{median}(x - |\operatorname{median}(x)|),$$

$\operatorname{PMAD}(x, y)$ is the pooled version of $\operatorname{MAD}$:

$$\operatorname{PMAD}(x, y) = \sqrt{\frac{(n_x - 1) \operatorname{MAD}^2_x + (n_y - 1) \operatorname{MAD}^2_y}{n_x + n_y - 2}},$$

$\operatorname{median}$ is the traditional sample median,

## Case study

Let us consider the following three samples (inspired by a real set of data samples):

$$\begin{split} x = \{ & 298, 297, 314, 312, 299, 301, 295, 295, 293, 293, 293, 293, 293, 292, 295,\\ & 293, 295, 293, 292, 295, 293, 293, 293, 299, 295, 304, 301, 296, 327, 294,\\ & 294, 293, 293, 293, 293, 293, 293, 292, 293, 292, 293, 294, 292, 294, 294,\\ & 294, 293, 293, 293, 293, 292, 294, 293, 296, 294, 299, 292, 293, 293, 294,\\ & 292, 293, 293, 292, 294, 292, 292, 293, 293, 292, 292, 292, 294, 293, 293\}, \end{split}$$ $$\begin{split} y_A = & \{ 2641, 30293, 27648 \},\\ y_B = & \{ 2641, 175631, 532991 \}. \end{split}$$

We may expect that the effect size between $x$ and $y_B$ should be larger than the effect size between $x$ and $y_A$. However, it is not true for the previously defined $d(x, y)$ measure:

$$d(x, y_A) \approx 63.8, \quad d(x, y_B) \approx 6.2.$$

Surprisingly, $d(x, y_B)$ is actually ten times smaller than $d(x, y_A)$. Moreover, $d(x, y_B) \approx 6.2$ does not always mean a truly significant change between medians in the nonparametric case. Such a situation arises because of the enormous median absolute deviation of $y_B$: $\operatorname{MAD}(y_B) = 172990$. Regardless of the small size of $y_B$, the pooled median absolute deviation is heavily affected: $\operatorname{PMAD}(x, y_B) \approx 28062.68$. Since it is used as the denominator in $d(x, y)$ equation, a huge value of $\operatorname{PMAD}$ leads to a small value of estimated effect size.

The described problem can often be observed when the second sample contains a small number of elements from a heavy-tailed distribution. In such cases, it is better to use the previously defined Glass’s delta-consistent approach. Here are the corresponding results:

$$\Delta(x, y_A) \approx 27355, \quad \Delta(x, y_B) \approx 175338.$$

As we can see, $\Delta(x, y_A) < \Delta(x, y_B)$, which matches our expectation. Moreover, $\Delta(x, y_B) \approx 175338$, which is much more impressive than $d(x, y_B) \approx 6.2$: the estimated change is truly significant.