People don't always tell the whole truth on dating websites, embellishing the truth to make themselves more desirable. This example of how OK Cupid users lie about their heights is a good example for conceptually explaining null hypothesis testing, t-tests, and normal distributions.
I can think of a number of ways to use this example:
-Null hypothesis testing/effect sizes, in general: Do you control and experimental groups overlap? By how much? Essentially, we are more likely to find significance/large effects the less they overlap. These two figures demonstrate this idea pretty nicely.
-A conceptual example of one-sample t-test. The CDC can provide us with a given number representing average male or female height, which is our known mean/mu. We could then test that number against all of the male or female heights reported by OK Cupid Users. Well, not really test, as we don't have the raw data, but it conveys the idea conceptually.
-This might even make a good example for Social or Evolutionary Psychology.
-Higher level statistics classes could also learn from the code he the author generously shared.
-I remember learning in graduate school that men typically round up when researchers ask them their number of sexual partners, and women typically round down. We can add height to the list of things that people fib about, especially within the context of seeking out a dating partner.
More of Cheng's work can be viewed here.
So, Cheng, article author and data enthusiast, looked through OK Cupid data. In this article, she describes a few different findings, but I'm going to focus on just one of them: She looked at users' reported heights. And she found a funny trend. Both men and women seem to report that they are taller than they actually are. How do we know this? Well, the CDC collects information on human heights so we have a pretty good idea of what average heights are for men and women in the US. And then the author compared the normal curve representing human height to the reported height data from OK Cupid Users. See below...
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| From http://nycdatascience.com/okcupid-scraper/, by Fangzhou Cheng |
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| From http://nycdatascience.com/okcupid-scraper/, by Fangzhou Cheng |
I can think of a number of ways to use this example:
-Null hypothesis testing/effect sizes, in general: Do you control and experimental groups overlap? By how much? Essentially, we are more likely to find significance/large effects the less they overlap. These two figures demonstrate this idea pretty nicely.
-A conceptual example of one-sample t-test. The CDC can provide us with a given number representing average male or female height, which is our known mean/mu. We could then test that number against all of the male or female heights reported by OK Cupid Users. Well, not really test, as we don't have the raw data, but it conveys the idea conceptually.
-This might even make a good example for Social or Evolutionary Psychology.
-Higher level statistics classes could also learn from the code he the author generously shared.
-I remember learning in graduate school that men typically round up when researchers ask them their number of sexual partners, and women typically round down. We can add height to the list of things that people fib about, especially within the context of seeking out a dating partner.
More of Cheng's work can be viewed here.
Link to Cheng is down!
ReplyDeleteHmmm...can't find it at the original website, will edit this post with the original author's findings. Thanks for the heads up, Anonymous Friend!
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