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Updated example of using baby names to illustrate bimodal distributions.

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...

Subways! Murder! Absolute vs. relative risk!

When I teach the basics of probability in Intro Stats, I always emphasize absolute vs. risk. I am delighted to have a brand new example. Thanks to Dr. Sy Islam for sending it my way.

Here is the headline from The New York Post:




So, one murder is too many murders. A 60% increase feels very scary. Because relative risk is always the scary risk. 

Since this reporting concerns murder, the reporting itself should be serious, right? Well, what were the absolute values for subway murders? I mean, The New York Post would never, ever want to instill fear in people, right? Well, despite this headline, The New York Post, much to its credit, did include the actual data in the actual article:


Eight murders, versus five in the previous years. Which is too terrible, but not nearly as frightening as a (checks notes) SOARING 60% increase.

Anyway. Ta-da! Use this in your class. 

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