Tag: cohensd

Visualizing and interpreting Cohen’s d effect sizes

Visualizing and interpreting Cohen’s d effect sizes

Cohen’s d (wiki) is a statistic used to indicate the standardised difference between two means. Resarchers often use it to compare the averages between groups, for instance to determine that there are higher outcomes values in a experimental group than in a control group.

Researchers often use general guidelines to determine the size of an effect. Looking at Cohen’s d, psychologists often consider effects to be small when Cohen’s d is between 0.2 or 0.3, medium effects (whatever that may mean) are assumed for values around 0.5, and values of Cohen’s d larger than 0.8 would depict large effects (e.g., University of Bath).

The two groups’ distributions belonging to small, medium, and large effects visualized

Kristoffer Magnusson hosts this Cohen’s d effect size comparison tool on his website the R Psychologist, but recently updated the visualization and its interactivity. And the tool looks better than ever:

Moreover, Kristoffer adds some nice explanatons of the numbers and their interpretation in real life situations:

If you find the tool useful, please consider buying Kristoffer a coffee or buying one of his beautiful posters, like the one above, or below:

Frequentisme betekenis testen poster horizontaal image 0

By the way, Kristoffer hosts many other interesting visualization tools (most made with JavaScript’s D3 library) on statistics and statistical phenomena on his website, have a look!

Robust Effect Sizes for Independent Group Comparisons

Robust Effect Sizes for Independent Group Comparisons

Guillaume Rousselet explains how and when group comparisons with Cohen’s d fail, and what robust statistics one could use instead:

basic statistics

When I was an undergrad, I was told that beyond a certain sample size (n=30 if I recall correctly), t-tests and ANOVAs are fine. This was a lie. I wished I had been taught robust methods and that t-tests and ANOVAs on means are only a few options among many alternatives. Indeed, t-tests and ANOVAs on means are not robust to outliers, skewness, heavy-tails, and for independent groups, differences in skewness, variance (heteroscedasticity) and combinations of these factors (Wilcox & Keselman, 2003; Wilcox, 2012). The main consequence is a lack of statistical power. For this reason, it is often advised to report a measure of effect size to determine, for instance, if a non-significant effect (based on some arbitrary p value threshold) could be due to lack of power, or reflect a genuine lack of effect. The rationale is that an effect could be associated with a sufficiently large effect…

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