The effect size _d_ tries to quantify the difference between two distributions by reporting the difference between the distributions' means in _standardized_ units—units that have been scaled to take into account how "spread out" the data is. This gives us a common reference scale for _how big_ a given statistical difference is. Height is measured in meters, and "Agreeableness" in the [Big Five personality model](https://en.wikipedia.org/wiki/Big_Five_personality_traits) is an abstract construct that doesn't even have natural units, and yet there's still a meaningful sense in which we can say that the sex difference in height (_d_≈1.7) is "about three times larger" than the sex difference in Agreeableness (_d_≈0.5).[ref]Yanna J. Weisberg, Colin G. DeYoung, and Jacob B. Hirsh, ["Gender Differences in Personality across the Ten Aspects of the Big Five"](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3149680/), Table 2[/ref]
-Cohen's _d_ is computed as the difference in group means, divided by the square root of the pooled variance. Thus, holding _actual_ sex differences constant, more measurement error means more variance, which means smaller values of _d_. Here's some toy Python code illustrating this effect:
+Cohen's _d_ is computed as the difference in group means, divided by the square root of the pooled variance. Thus, holding _actual_ sex differences constant, more measurement error means more variance, which means smaller values of _d_. Here's some toy Python code illustrating this effect:[ref]Special thanks to Tailcalled for [catching a bug](http://unremediatedgender.space/source?p=Ultimately_Untrue_Thought.git;a=commitdiff;h=c5158d9a6feaa7ed5c770e6ace83d7e7ba2451e6) in the initially published version of this code.[/ref]
```python
from math import sqrt
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