Sometimes I see a “top X” list that’s, shall we say, all male (think lists of top scientists, recommended SFF authors, etc.). And when people object, others defend against the objection with, “But what if the field’s mostly male???”
There are a whole host of problems with this, but I’m not really going to get into them here. I’m just going to do some math.
As far as I can figure, my starting assumptions are only that (1) we expect Top-X lists to sample gender randomly — that is, that a male person in the field is not automatically expected to be better than a non-male person already in the field, and (2) there is no institutional sexism going on beyond whatever might cause the gender skew in the first place.
If we see unlikely Top-X lists, one of these assumptions must be wrong.
Let’s look at a Top-10 list.
The Approximate Probability of an All-Male Top-10 List
If a field is 50% male, the likelihood that a Top-10 list will be entirely male is .098%.
If a field is 60% male, the likelihood that a Top-10 list will be entirely male is .60%.
If a field is 70% male, the likelihood that a Top-10 list will be entirely male is 2.8%.
If a field is 75% male, the likelihood that a Top-10 list will be entirely male is 5.6%.
If a field is 80% male, the likelihood that a Top-10 list will be entirely male is 11%.
If a field is 90% male, the likelihood that a Top-10 list will be entirely male is 35%.
I note that even in the most extreme case — 90% male is a VERY extreme gender skew — only about 1/3 of Top 10 lists would be expected to be composed entirely of men.
This math is very easy, by the way, and you can replicate it quickly for any Top-X list and any percentage of men. If m is the percentage of men written as a decimal, just raise m to the power of X. So to find the likelihood a Top-25 list is all-male if you suspect a field of being 3/4 male, you would do (.75)^25 (which incidentally equals .075% — in other words, it’s EXTREMELY unlikely for even a field that is 3/4 male to have a Top-25 list that is all-male).
Whether any skewed percentages are a result of other biases in the first place is, of course, another discussion. But if you find yourself with extremely probabilistically unlikely Top-X lists even given skewed percentages, then maybe it’s worth thinking about why that might be. And if the “you” in question is a magazine, bookstore display rack, publisher’s promo list, other Official Book Industry Recommendation List, review blog, fanzine, etc. . . . it might be worth urging your staff to be somewhat less unlikely.
Math note: I’ve sampled with replacement here, on the assumption that the field is big enough relative to X that removing up to X people for the list has not changed the gender ratio among the population of people not on the list. If you have a (relatively) small field or a large list, the math becomes more complicated.
Comments are open, but I may not have time at the moment to respond (I still haven’t caught up on the comments for my LAST gender and math post, argh I am the worst!). Comments will still be moderated if necessary — please be kind to each other.
eta: Even though I triple-checked, I made a copy-paste error — the 50% line initially read .0098% instead of .098%. SORRY!