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...
I love dragging on bad science reporting as much as anyone, but I must give All Of The Credit to the Washington Post and its excellent, data-centered reporting on the opioid epidemic. It is a thing of beauty.
How to use in class:
1) Broadly, this is a fine example of using data to better understand applied problems, medical problems, drug problems, etc.
2) Specifically, this data can be personalized to your locale via WaPo's beautiful, functional website.
3) After you pull up you localized data, descriptive data abound...# of pills, who provided them, who wrote the scripts (y'all...Frontier Pharmacy is like two miles from my house)...
4) Everyone teaches about frequency tables, right? Here is a good example:
5) In addition to localizing this research via the WaPo website, you can also personalize your class by looking for local reporting that uses this data. For instance, the Erie newspaper reporter David Bruce reported on our local problem (.pdf of the report here).
You can also use the big map to find more localized data.
6) You know something is important and damning if wealthy stakeholders try to keep you away from it: The government didn't just offer up this data. The press had to fight for it. This is another good example of large organizations trying to hide unflattering data or influence data (See: Puerto Rico, NFL, Greece, fossil fuel industry, etc.).f
7) Here is an NPR summary of the fight for this data.
**Most of this is behind paywalls. Because reporters have to eat, too. And WaPo has a bunch of lawyers to pay. There are educator discounts available for WaPo and several free views of news stories as well.
How to use in class:
1) Broadly, this is a fine example of using data to better understand applied problems, medical problems, drug problems, etc.
2) Specifically, this data can be personalized to your locale via WaPo's beautiful, functional website.
3) After you pull up you localized data, descriptive data abound...# of pills, who provided them, who wrote the scripts (y'all...Frontier Pharmacy is like two miles from my house)...
4) Everyone teaches about frequency tables, right? Here is a good example:
5) In addition to localizing this research via the WaPo website, you can also personalize your class by looking for local reporting that uses this data. For instance, the Erie newspaper reporter David Bruce reported on our local problem (.pdf of the report here).
You can also use the big map to find more localized data.
6) You know something is important and damning if wealthy stakeholders try to keep you away from it: The government didn't just offer up this data. The press had to fight for it. This is another good example of large organizations trying to hide unflattering data or influence data (See: Puerto Rico, NFL, Greece, fossil fuel industry, etc.).f
7) Here is an NPR summary of the fight for this data.
**Most of this is behind paywalls. Because reporters have to eat, too. And WaPo has a bunch of lawyers to pay. There are educator discounts available for WaPo and several free views of news stories as well.