UC-Davis professor and perennial noticer of gender imbalance at conferences Jonathan Eisen received an e-mail invitation and call for submissions to the 2013 Winter Q-Bio Meeting: Quantitative Biology on the Hawaii Islands. Nice, right? Sun, science, slate of speakers almost exclusively composed of dudes. (On the plus side, Dr. Lahav, at least you’ll never have to wait in line for the bathroom.)
That is a 25:1 ratio. Pathetic. Embarrassing. The sponsors – UC San Diego’s Division of Biological Sciences and BioCircuits Institute, San Diego Center for Systems Biology, the University of Hawaii and the Office of Naval Research – should all be ashamed.
One female speaker out of 26. (Eisen tends to shorthand to XX/XY, but unless he’s doing some pretty intrusive genetic testing at these conferences, I’m guessing he means female/male.) Even given current gender disparities in STEM fields, that still seems a bit more disparate than can be attributed to chance. So Eisen submitted his own abstract for the meeting:
A quantitative analysis of gender bias in quantitative biology meetings
(Or, The probability of having one out of twenty-six participants at a scientific meeting be female)
Scientific conferences have key participants which I define to be the speakers and the organizers. Such key participants can be divided into two main classes based on gender: male and female, which I denote here as M and F, respectively (I realize there are other gender classes and I regretfully am not including them here). The number of key participants (which I denote as KP) for conferences varies significantly. For this analysis I focused on meetings with KP = 26. This value was selected for multiple reasons, including (a) that it is the number of letters in the English alphabet (b) that its factors include the number 13 which I like, and (3) because in email announcements for this meeting KP= 26. I sought to answer a relatively simple question – what is the probability that, for a meeting with KP=26, that F = 1. I chose this because this seemed extreme and because F=1 in the email announcements for this meeting.
[math, math, mathity math-math…]
Given that for p = 0.2 the Pr (F=1) < 0.05 I therefore conclude that the null hypothesis (that having one female out of 26 key participants) can be rejected - and that this meeting has a biased ratio of males: females.
Results, I’m sure, that shock us all.
Disheartening? Sure. But at least now, when someone starts claiming there isn’t gender discrimination at conferences, you can hit ‘em with some math. And if that doesn’t work, just sigh and pull out your bingo card. (Click to embiggen.)