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 radio story by Mara Liasson (reporting for NPR) discusses the surprising primary loss of former Republican House Majority Leader Eric Cantor. It was surprising because internal polling conducted by Cantor's team gave him an easy win, but he lost out to a Tea Party favorite, David Brat. The story goes on to describe why it is becoming increasingly difficult to conduct accurate voter polling via telephone and the internet.
Some specific points from this story that teach students about sampling techniques:
1) Sample versus population: One limitation of polling data is the fact that many telephone call-based sampling techniques include landlines and ignore the growing population of people who only have cell phones.
2) Response rates for political polling are on a decline, making the validity of the available sample shrink.
3) Robocalls, while less expensive, have no way of validating that an actual registered voter is responding to the questions. Additionally, restrictions on placing robocalls to cell phones (but not landlines) create more difficulties for pollsters.
I used this example as a way of introducing sampling error and, eventually, the distribution of the sampling mean/standard error.
I emphasized that Cantor's polling had been conducted by high-end polling firm McLaughlin and Associates (source). I also explained just how powerful Eric Cantor was and if an extraordinarily rich and powerful person using a very expensive polling firm has to contend with sampling error, then what does that say for the rest of us?