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 story discusses the many levels of analysis required to get to the bottom of the hypothesis stated in the title of this story. For instance, are cigarettes or the patch better for mom? The baby? If the patch isn't great for either but still better than smoking, what sort of advice should a health care provider give to their patient who is struggling to quit smoking? What about animal model data? I think this story also opens up the conversation about how few medical interventions are tested on pregnant women (understandably so), and, as such, researchers have to opt for more observational research studies when investigating medical interventions for protected populations.