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...
Refutation to Anti-vaccination Memes came up with this excellent illustration to explain why anti-vaxxers shouldn't claim a "win" just because more vaccinated people than unvaccinated people get sick during an outbreak.
This example has a bit more credence if paired with actual immunization rate/infection rate data. For instance, in a case when an outbreak has occurred, and most infected are immunized, but there were still some un-immunized individuals.
To further this case, yes, most people in America are immunized. However, here is an example of an outbreak that has been linked to un-vaccinated folks.
How to use it in class:
-Base rate fallacy (which DOES matter when making an argument with descriptive stats!)
-Relative v. absolute risk.
-Making sense of and contextualizing descriptive statistics.