Monday, October 19, 2015

A Statistical Argument for Not Being Nervous on First Dates

A few months ago I became single for the first time in four years. I went from studying online dating to being a datapoint myself [1]. This has made me think more urgently about questions I once considered only abstractly. Today I write about the connection between testing statistical hypotheses and testing romantic attraction.

Statisticians love to develop multiple ways of testing the same thing. If I want to decide whether two groups of people have significantly different IQs, I can run a t-test or a rank sum test or a bootstrap or a regression. You can argue about which of these is most appropriate, but I basically think that if the effect is really statistically significant and large enough to matter, it should emerge regardless of which test you use, as long as the test is reasonable and your sample isn’t tiny. An effect that appears when you use a parametric test but not a nonparametric test is probably not worth writing home about [2].

A similar lesson applies, I think, to first dates. When you’re attracted to someone, you overanalyze everything you say, spend extra time trying to look attractive, etc. But if your mutual attraction is really statistically significant and large enough to matter, it should emerge regardless of the exact circumstances of a single evening. If the shirt you wear can fundamentally alter whether someone is attracted to you, you probably shouldn’t be life partners.

You can argue against this by pointing out cases where a tiny detail does matter because it prevents you from having any future interactions: for example, you foolishly wear your XL Chess Team sweatshirt to the bar and your would be Lothario never bothers to approach you and thereby discover that you look much better with it off.

This is a risk. In statistical terms, a glance at across a bar doesn’t give you a lot of data and increases the probability you’ll make an incorrect decision. As a statistician, I prefer not to work with small datasets, and similarly, I’ve never liked romantic environments that give me very little data about a person. (Don’t get me started on Tinder. The only thing I can think when I see some stranger staring at me out of a phone is, “My errorbars are huge!” which makes it very hard to assess attraction.)

Even on a longer date, there’s some risk that a disaster at the beginning will ruin your subsequent interactions. If you start by asking “how’s your relationship with your mother?”, you’ve torpedoed your chance to have a truly intimate conversation about how she ran off to train monkeys.

Still, I’m sticking to the principle that if your romance-to-be is statistically robust, whether you wear makeup or the moon is full should make no more difference than whether you compute the Spearman or Pearson correlation. (And if your date asks you if you want to bootstrap, the answer is always, of course, yes.)

I think there’s even an argument for being deliberately unattractive to your date, on the grounds that if they still like you, they must really like you. Imagine a cliched rom-com disaster [3]: you vomit on your date. This isn’t sexy. On the other hand, someone who finds you attractive after that is much more likely to still find you attractive when you’re puking during pregnancy or chemotherapy [4]. This is somewhat analogous to using a statistical test that makes very weak assumptions (here's one example): if the test yields positive results, you can have high confidence they're real.

Please don’t send me angry emails when you take this post too seriously and the love of your life spurns you because you didn’t shower for a week before your date. But I’d welcome your thoughts in the comments or via email. (Also hit me up if you have ideas for statistical projects that I can only conduct while single.)

[1] I recently received an email from a Stanford professor in a similar situation: his marriage broke up after 20 years, and he responded by writing a book about the connections between economics and dating.
[2] An economics friend points out a corollary to this principle: be suspicious of analyses that use really convoluted tests when it seems like simple ones should do, because that might indicate that the simple ones didn’t produce the results they’re reporting.
[3] 10 Things I Hate About You, 50 Shades of Grey, Mean Girls. What’s with this trope, and why are the pukers always female?
[4] You’re calling me crazy and I’m kind of kidding, but I’d also argue that the idea of testing one’s partner is a socially accepted one. (My scholarly attempt to do a lit review on this question -- I Googled “make them work for it” -- yielded this text. You’re welcome.) There are many bad reasons people are told to defer sleeping with someone, but a not-so-bad-one, from a probabilistic standpoint, is that someone who will wait might be more likely to really like you.

Friday, October 16, 2015

A Better Way to Conduct Sexual Assault Surveys

Shengwu Li and I argue in the Washington Post that universities who conduct sexual assault surveys often misunderstand the basic statistical goal: not to get as many students as possible to answer the survey, but to get an unbiased sample of reasonable size. We propose a method for doing this.

Saturday, October 10, 2015

Analysis of the Online Abortion Debate

Brian Clifton, Gilad Lotan and I published an analysis of the fierce online debate about abortion. We visualize the spread of the hashtags #ShoutYourAbortion and #ShoutYourAdoption, develop a method for classifying tweeters as pro-choice or pro-life, and show that we can often predict someone's stance with high accuracy -- without ever reading their profile or tweets. Here is Brian's beautiful visualization of the spread of the hashtag.