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...
Yelp distribution visualization, posted by redditor minimaxir This data distribution example comes from the subreddit r/dataisbeautiful (more on what a reddit is here ). This specific posting (started by minimaxir) was prompted by several histograms illustrating customer ratings for various Yelp (customer review website) business categories as well as the lively reddit discussion in which users attempt to explain why different categories of services have such different distribution shapes and means. At a basic level, you can use this data to illustrate skew, histograms, and normal distribution. As a more advanced critical thinking activity, you could challenge your students to think of reasons that some data, like auto repair, is skewed. From a psychometric or industrial/organizational psychology perspective, you could describe how customers use rating scales and whether or not people really understand what average is when providing customer feedba...