Sample size matters.
By Tess Rinearson (Sophomore, Carnegie Mellon’s School of Computer Science)
“This Marine Infantry Course Proved Too Much For Its First Two Women Applicants.”
“Female Lieutenants Flunk Marine Corps’ Fierce Infantry Training.”
“Second Female Marine Fails Grueling Infantry Officer Course.”
Those were some of the headlines after the failure of an “experiment” in the Marines to introduce women into their infantry officer course last year.
For the first time, the Marines admitted women – just two of them – to its grueling Infantry Officer Training Program. They both failed, one of them on the very first day, and the media was all over it. But what none of these headlines mention is that 26 of the 107 of men also flunked out – on the first day alone. That’s to say nothing of the others who end up dropping out as the grueling course continues.
In other words, any two men could have very easily failed as well. But instead, it’s easy to walk away with the impression that women can’t cut it as Infantry Officers in the Marines.
This is the problem with underrepresentation: Anyone in a minority suddenly becomes representative of that whole minority.
This is bad for many reasons. For one thing, this is how stereotypes start.
Underrepresentation also means that a lot of people are misrepresented. To use a personal example, I know there are many women in computer science who would hate to be represented by me. I have particular views on things, and many women in CS agree with them, but plenty don’t.
Additionally, underrepresentation puts immense pressure on people in minority groups. One of my friends told me that she struggled a bit when she started taking computer science classes, and felt so ashamed that she thought about switching majors: “I thought, ‘I am letting down women in computer science.’”
Can you even imagine a man thinking that? “I did poorly on that last test, so I am letting down men in computer science.”
But, of course, this isn’t limited to women. A male, African American friend told me that he drove himself crazy trying to be a strong representative in computer science before he realized he just had to do the best he could.
And that’s all you can do – try your best, and hope that you are recognized as an individual rather than an unsuspecting representative. And, conversely, recognize that you can’t draw reasonable conclusions about a whole group from just a few people.
To use a favorite refrain of science students: Sample size matters. A woman may have flunked out on the first day of Infantry Officer Training, but so did 26 men.
This post was originally posted at Tess Rinearson’s blog.
Women 2.0 readers: How can we offset imposter syndrome or negative stereotypes of women in tech? Let us know in the comments.
About the guest blogger: Tess Rinearson is a 19-year-old computer science student at Carnegie Mellon University. She spent the summer of 2012 as an Software Development Engineering intern at Microsoft. In the past, she’s worked at startups including CloudMine and Intersect.com, and at game company Valve Software. In her spare time, she works on her own side project and goes to hackathons (sometimes at the same time). Follow her on Twitter at @temiri.
