But doesn't a similar argument hold for non-error sources of variance that are "orthogonal" to the group difference? (Sorry, I know this is vague; I'm writing to the list in case any Actual Scientists can spare a moment to help me make my intuition more precise.) Like, suppose performance on some particular cognitive task can be modeled as the sum of the general intelligence factor (zero or negligible sex difference[^jensen]), and a special ability factor that does show sex differences. Then, even with zero _measurement_ error, _d_ would underestimate the difference between women and men _of the same general intelligence_.
-[^jensen]: Arthur Jensen, _The g Factor_, Chapter 13
+[^jensen]: Arthur Jensen, _The g Factor_, Chapter 13: "Although no evidence was for sex differences in the mean level of _g_ or in the variability of _g_, there is clear evidence of marked sex differences in group factors and in test specificity. Males, on average, excel on some factors; females on others. [...] But the best available evidence fails to show a sex difference in _g_."
```python
def performance(g, σ_g, s, n):
projects / Ultimately_Untrue_Thought.git / blobdiff