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...
This example gets students thinking about how we operationalize variables. Psychologists operationalize a lot of abstract stuff. Intelligence. Grit. But what about something that seems more firmly grounded and countable, like whether or not a crime meets the criteria for a a mass shooting? How do we define mass shooting? As shared in this article by Chris Wilson for Time Magazine , the official definition is 1) three or more people 2) killed in a public setting. That is per the current federal definition of a mass shooting . But that isn't universally excepted by media outlets. The article shares different metrics used for identifying a mass shooting, depending on what source is being used. Whether or not to include a dead shooter towards the total number killed. Whether or not the victims were randomly selected. I think the most glaring example from the article has to do with the difference that this definition makes on mass shooting counts: You could also discuss wi...