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...
TL;DR: One way to avoid government regulation is by simply refusing to share data that may lead to government regulation (and safer trains). I'm looking at you, railroads. _____________________________________________________________________________ Not every example I post syncs directly to the typical Psychological Statistics curriculum. I also post about statistical literacy. Like why data matters and counts. And how very, very simple data could help illuminate and solve real-world problems, but only if we can access that data. I get good and mad at organizations that avoid responsibility by manipulating and/or withholding data. See: Organizations that share data but in a functionally inaccessible way. Also, I created a spreadsheet (of course I did) containing several examples of times when large organizations goofed around with data so they wouldn't get sued. It looks like I should add rail roads to this list. Aside: I grew up not 10 miles from the world-famous Horsesho...