HOW TO CALL B.S. ON BIG DATA: A PRACTICAL GUIDE

https://goo.gl/z9ocC1

Bergstrom believes that calling bullshit on data, big or otherwise, doesn’t require a statistics degree—only common sense and a few habits of mind. “You don’t have to understand all the gears inside a black box in order to evaluate what you’re being told,” he said. For those who were unable to enroll in INFO 198/BIOL 106B this spring, here is some of his and West’s advice:

• Recognize that bullshitters are different from liars, and be alert for both. To paraphrase the philosopher Harry Frankfurt, the liar knows the truth and leads others away from it; the bullshitter either doesn’t know the truth or doesn’t care about it, and is most interested in showing off his or her advantages.

• Upon encountering a piece of information, in any form, ask, “Who is telling me this? How does he or she know it? What is he or she trying to sell me?” (Journalists have their own versions of these questions.) If you’d ask it at a car dealership, West suggested to the students, you should ask it online, too.

• Remember that if a data-based claim seems too good to be true, it probably is. Conclusions that dramatically confirm your personal opinions or experiences should be especially suspect. Bergstrom pointed the class to a study that compared the language used in letters of recommendation for male and female applicants for chemistry jobs. The researchers hypothesized that the letters for men would use more “ability” words (“talented,” “smart”), whereas those for women would use more “grindstone” words (“hardworking,” “conscientious”). Though they found no evidence to back up the idea, readers aware of the very real gender bias in scientific fields inadvertently tweeted the hypothesis, not the results.


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