The thing about my way of teaching stats is that my cool, novel examples get old really fast. Like, pop culture is on a thirty-day cycle. Anyway, YEARS ago, 2014 to be exact, I blogged about illustrating bimodal data distributions using baby name data from the Social Security Administration. Specifically, when an older name comes back into vogue, it has a bimodal distribution when you plot time on the X axis. I stand by this example. However, all of the data illustrations come from 2014. When our traditionally aged college students were 6 or 7. Which makes this example feel a little stale. However, here is some data, courtesy of Redditor fevaah , showing old names that are back in vogue through 2025 ! And Eleanor, Violet, Nora, and Hazel are here, illustrating bimodal distributions for a new generation. If you used my old baby-name data, this is a quick, easy refresh for your lectures (or a fun new addition if you didn't). If this idea was help...
Michael Hobbes shared a Tweet from Nick Gillespie. That Tweet was about an essay from The Bulwark . That Tweet plays fast and loose with Likert-type scale interpretation. The way Hobbes and his Twitter followers break down the issues with this headline provides a lesson on how to examine suspicious research clickbait that doesn't pass the sniff test. First off, who says "close to one in four"? And why are they evoking the attempt on Salman Rushdie's life, which did not happen on a college campus and is unrelated to high-profile campus protests of controversial speakers? Hobbes dug into the survey cited in the Bulwark piece. The author of the Bulwark piece interpreted the data by collapsing across response options on a Likert-type response scale. Which can be done responsibly, I think. "Very satisfied" and "satisfied" are both happy customers, right? But this is suspicious. Other Twitter users questioned the question and how it may leave room for i...