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...
A great example using a topic relevant to your students (video games), involving developmental psychology (the effect of violent media on children), and a modern event (Scalia's passing) in order to demonstrate the importance of both research psychology as well as statistics. This article extensively quote Scalia's majority opinion regarding Brown vs. Entertainment Merchants Association, a 2010 U.S. Supreme Court case that decided against California's attempt to regulate the sale of violent video games to minors (the full opinion embedded in the article). Why did Scalia decide against regulating violent video games in the same manner that the government regulates alcohol and cigarette sales? In part, because research and statistics. Of particular use to an instructor of statistics are sections when Scalia cites shaky psychological research and argues that correlational research can not be used to make causal arguments... ...Scalia also discusses effect sizes... ...