Sex and… data science???

Last week we got a nice heads’ up from Tracy, who shared this article in the NY Times. It looks at the sexes from a data science perspective. Are men really from Mars? Are women really from Venus? What happens when you strip away the stereotypes and human intuition about how men and women are different (or not) and look at the sexes as a data scientist would?

The study was done by Bobbi Carothers men v women(a senior data analyst at Washington University) and Harry Reis (a psychology professor at the University of Rochester). It used a data science technique called taxometric analysis to crunch data from 13 studies, looking at 122 attributes of men and women from a sample of more than 13,000 individuals.

A few highlights:

Despite many stereotypes, both men and women are basically the same when it comes to their interest in casual sex, the allure of a potential mate’s virginity, general assertiveness and the value they place on close friendships. They also share the frequency of science-related activities in their lives – not the sexiest factoid, but kind of interesting anyway.

That said, men and women were statistically different in some other ways. For example, there are differences where physical size is an issue (ex. athletics). Men really do seem to play more video games. Women really do seem to do more scrapbooking.

While there are a lot of other fascinating tidbits in the study, the main takeaway seems to be: men and women are a lot more alike than some stereotypes suggest. Who knew data science could be so sexy?

 

This Post Has 5 Comments

  1. who knew? Wwow!

  2. fact v fiction. love it

  3. Many people misinterpret statements of likelihood and probability as a sign of weakness or uncertainty in scientific results. However, the use of statistical methods and probability tests in research is an important aspect of science that adds strength and certainty to scientific conclusions. For example, in 1843, John Bennet Lawes , an English entrepreneur, founded the Rothamsted Agriculture Experimental Station in Hertfordshire, England to investigate the impact of fertilizer application on crop yield. Lawes was motivated to do so because he had established one of the first artificial fertilizer factories a year earlier. For the next 80 years, researchers at the Station conducted experiments in which they applied fertilizers, planted different crops, kept track of the amount of rain that fell, and measured the size of the harvest at the end of each growing season. By the turn of the century, the Station had a vast collection of data but few useful conclusions: one fertilizer would outperform another one year but underperform the next, certain fertilizers appeared to affect only certain crops, and the differing amounts of rainfall that fell each year continually confounded the experiments ( Salsburg, 2001 ). The data were essentially useless because there were a large number of uncontrolled variables .

  4. The damning problem to this lack of rigor is that the facts will come out eventually. People will try these weak recommendations and discover their mixed or lackluster results. And while we may get away with it in the short run, we all suffer from an erosion of the faith in real data science.

  5. “But really, what is data science? It’s not like people went to school to major in it,” a third chimed in.

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