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...
Interpreting effect sizes requires more than Rules of Thumb for interpretation. Interpretation requires deeper knowledge about the investigated topic, an idea we must convey to our students. For example, in presidential elections in the United States, the winner is usually selected by a slim margin. As such, if you can get even small numbers of voters who don't usually vote to vote, it can have a large real-world effect on an election. This is what Vote Forward is trying to do, and I'll explain how you can use their work to explain effect sizes in your stats classes. This is Vote Foreward : Okay. So they are organizing letter-writing campaigns in advance of the 2020 General Election. NOTE: The organization is left-leaning, but many of its campaigns ask letter-writers to share non-partisan messages. Vote Forward has tested whether or not writing letters to unlikely voters actually gets people to vote, and they shared the results of those efforts: Their findings, which aren...