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...
When we talk false positives in psych stats, it is usually in the context of NHST, which is abstract and tricky to understand, no matter how many normal curves you draw on the dry erase board. We also tend to frame it in really statsy terms, like alpha and beta, which are also tricky and sort of abstract, no matter how many times you repeat .05 .05 .05. In all things statistics, I think that abstract concepts are best understood in the context of real-life problems. I also think that statistics instructors need to emphasize not just statistics but statistical thinking and reasoning in real life. To continue on a theme from my last post, students need to understand that the lessons in psych stats aren't just for performing statistics and getting a good grade, but also for improving general critical thinking and problem-solving in day to day life. I also think that our in-class examples can be too sterile. They may explain Type I/II error accurately, but we tend to only ask our stude...