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...
I maintain that relative versus absolute risk is a concept that we absolutely must teach in intro stats. I have given some examples of this before ( murder ! COVID !) but here is another one that hits home for my fellow Great Lakers. In particular, this one is for my friends in Detroit, Toledo, Cleveland, and Buffalo Up here in Erie, a common point of discussion is how frozen Lake Erie is. Because once it freezes, the lake's moisture no longer feeds Dread Lake Effect Snow. I like this example because you can easily perform the math in front of your class, demonstrating that 26.14% is 103.45% of 12.85%. At the same time, you have the visual to demonstrate that the vast majority of the lake was still unfrozen even with a 103.45% increase.