Gender Differences in Variability

… an Atlas concept in Socioeconomic and Political Context and Atlas105


Writing in Psychology Today, Michael Mills (reference below) describes the implications of the gender differences in the variability (rather than the average) of traits.

Mills writes:

“But there are two ways that the sexes could differ. They could be different in average group scores, as above. Or, they could have the same mean score, but differ in the amount of variation on the trait for each sex. That is, there can be a sex difference in within-sex variability, even when there is no sex difference in between-sex average group scores. …

“This finding of greater male variability in IQ scores has been replicated with many different populations and in more modern times. … You may recall that Larry Summers was forced to resign as President of Harvard University when many people simply misinterpreted his remark that males are more variable than are females on many traits.

“Most of his critics misunderstood his remarks and presumed that he was suggesting that males are on average more intelligent than females. Or, if they understood him correctly, some may have found it interesting that there were more intellectually deficient males than females, but the sex ratio at the other tail of the distribution was less palatable.”

Also in Psychology Today, Blair Dawson and Glenn Geher (reference below) write:

“Based on our examination of dozens of past studies that explore differences between males and females, there is evidence to suggest that while males and females may differ in terms of their statistical means in some domains as personality traits, causes of death, emotional intelligence, academic success, and so forth, a story that is just as compelling pertains to the fact that the sexes consistently differ in statistical variability – with, for a large number of variables, males demonstrating greater variability. …

“This finding of males showing more variability than females (across varied dimensions) is in line with a highly accepted biological concept, Bateman’s (1948) Principle, which is the idea that males of many species vary more in reproductive success than do females. Based on Bateman’s seminal research on fertility differences across the sexes in fruit flies, it turns out that males vary much more in the number of mates they acquire and offspring they produce compared with females – and this finding is a function of the fact that, as with humans, female fruit flies provide more necessary parental investment than do male fruit flies.

“Males and females may differ from one another on average – but, perhaps just as importantly, males and females seem to differ from one another in terms of how much they vary from other members of their own sex. Future research into the nature of male/female differences in any domain would be wise to incorporate an understanding of male/female differences in variability.”

More dumbbells but more Nobels: Why men are at the top

Helena Cronin, Co-Director of LSE’s Centre for Philosophy of Natural and Social Science, described some of the implications of gender differences in variability in her 2008 article entitled More dumbbells but more Nobels: Why men are at the top (reference below).

Cronin writes:

“I used to think that these patterns of sex differences resulted mainly from average differences between men and women in innate talents, tastes and temperaments. After all, in talents men are on average more mathematical, more technically minded, women more verbal; in tastes, men are more interested in things, women in people; in temperaments, men are more competitive, risk-taking, single-minded, status-conscious, women far less so. And therefore, even where such differences are modest, the distribution of these 3 Ts among males will necessarily be different from that among females –  and so will give rise to notable differences between the two groups. Add to this some bias and barriers – a sexist attitude here, a lack of child-care there. And the sex differences are explained. Or so I thought.

“But I have now changed my mind. Talents, tastes and temperaments play fundamental roles. But they alone don’t fully explain the differences. It is a fourth T that most decisively shapes the distinctive structure of male-female differences. That T is Tails – the tails of these statistical distributions. Females are much of a muchness, clustering round the mean. But, among males, the variance – the difference between the most and the least, the best and the worst – can be vast. So males are almost bound to be over-represented both at the bottom and at the top. I think of this as ‘more dumbbells but more Nobels’.

“Consider the mathematics sections in the USA’s National Academy of Sciences: 95% male. Which contributes most to this predominance – higher means or larger variance? One calculation yields the following answer. If the sex difference between the means was obliterated but the variance was left intact, male membership would drop modestly to 91%. But if the means were left intact but the difference in the variance was obliterated, male membership would plummet to 64%. The overwhelming male predominance stems largely from greater variance.

“Similarly, consider the most intellectually gifted of the USA population, an elite 1%. The difference between their bottom and top quartiles is so wide that it encompasses one-third of the entire ability range in the American population, from IQs above 137 to IQs beyond 200. And who’s overwhelmingly in the top quartile? Males. Look, for instance, at the boy:girl ratios among adolescents for scores in mathematical-reasoning tests: scores of at least 500, 2:1; scores of at least 600, 4:1; scores of at least 700, 13.1. …

“The upshot? When we’re dealing with evolved sex differences, we should expect that the further out we go along the right curve, the more we will find men predominating. So there we are: whether or not there are more male dumbbells, there will certainly be – both figuratively and actually – more male Nobels.

“Unfortunately, however, this is not the prevailing perspective in current debates, particularly where policy is concerned. On the contrary, discussions standardly zoom in on the means and blithely ignore the tails. So sex differences are judged to be small. And thus it seems that there’s a gaping discrepancy: if women are as good on average as men, why are men overwhelmingly at the top? The answer must be systematic unfairness – bias and barriers. Therefore, so the argument runs, it is to bias and barriers that policy should be directed. And so the results of straightforward facts of statistical distribution get treated as political problems – as ‘evidence’ of bias and barriers that keep women back and sweep men to the top. (Though how this explains the men at the bottom is an unacknowledged mystery.)

“But science has given us biological insights, statistical rules and empirical findings … surely sufficient reason to change one’s mind about men at the top.”

Men at the bottom

Writing in the New York Times, Thomas Edsall (link below) summarizes recent research on the growing gap between men and women at the low end of socioeconomic indicators. As the figure below shows, income disparity in the United States is increasing faster for men than for women.

Click for article

Atlas topic, subject, and course

Gender Inequality (core topic) in Socioeconomic and Political Context and Atlas105.


Michael Mills (2011), How Can There Still Be a Sex Difference, Even When There Is No Sex Difference?, Psychology Today, 26 June 2011, at, accessed 13 March 2017.

Blair B. Dawson and Glenn Geher (2014), Male/Female Differences in Variability Itself, Psychology Today, 4 October 2014, at, accessed 13 March 2017.

Helena Cronin (2008), More dumbbells but more Nobels: Why men are at the top, Edge, at, accessed 13 March 2017.

Thomas Edsall (2017), The Increasing Significance of the Decline of Men, New York Times, 16 March 2017, at, accessed 16 March 2017.

Page created by: Ian Clark, last modified 16 March 2017.

Image: Berkeley Lab, at, accessed 13 March 2017.