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...
We already knew that model/cookbook author Chrissy Teigen is really good at Twitter. We recently learned that, delightfully, she is also good at spotting misrepresented statistics. This came to light when she asked for help understanding the whole Jacob Wohl Debacle . She asked her Twitter followers for a clear, quick explanation of the whole deal. She didn't even @ Jacob, but Jacob got snippy and replied back with Google Trends data (how have I not blogged about Google Trends yet?) in an attempt to use data, beautiful data, in order to own Chrissy. And Chrissy was having none of it. Yes, her sweet burn is an inspiration to us all, but it also a good demonstration of that fact that the exact same data can be interpreted in two different ways. And jerks lie with data, too, and can lie with actual, truthful data. And Chrissy knows her way around a chart.