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...
So, this checks off a couple of my favorite requisites for a good teaching example: You can personalize it, it is contemporary and applicable, it illustrates a few different sorts of statistics. Climate Central wrote this article about first frost dates, and how those dates, and an increasing number of frost-free days, create longer growing seasons. The overall article is about how frosty the US is becoming as the Earth warms. They provide data about the first frost in a number of US cities. It even lists my childhood hometown of Altoona, PA, so I think there is a pretty large selection of cities to choose from. Below, I've included the screen grab for my current home, and the home of Gannon University, Erie, PA. The first frost date is illustrated with a line chart, but the chart also includes the regression line. Data for frosty, chilly Erie, PA The article also presents a chart that shows how frost is related to the length of the growing season in t...