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...
Aschwanden (for fivethirtyeight.com) did an extensive piece that summarizes that data/p-hacking/what's wrong with statistical significance crisis in statistics. There is a focus on the social sciences, including some quotes from Brian Nosek regarding his replication work. The report also draws attention to Retraction Watch and Center for Open Science as well as retractions of findings (as an indicator of fraud and data misuse). The article also describes our funny bias of sticking to early, big research findings even after those research findings are disproved (example used here is the breakfast eating:weight loss relationship).
The whole article could be used for a statistics or research methods class. I do think that the p-hacking interactive tool found in this report could be especially useful illustration of How to Lie with Statistics.
The "Hack your way to scientific glory" interactive piece demonstrates that if you fool around enough with your operationalized variables, you can probably, eventually, find a way to support your argument (AKA p-hacking). This example uses the question of whether to economy does better under Republican or Democratic leadership to prove a point. Within the tool, you can operationalize both leadership and measures of economic health in a number of different ways (via the check boxes on the left), resulting in a number of different outcomes.
The "Hack your way to scientific glory" interactive piece demonstrates that if you fool around enough with your operationalized variables, you can probably, eventually, find a way to support your argument (AKA p-hacking). This example uses the question of whether to economy does better under Republican or Democratic leadership to prove a point. Within the tool, you can operationalize both leadership and measures of economic health in a number of different ways (via the check boxes on the left), resulting in a number of different outcomes.
This is whole article is useful in the classroom simply as a way to discuss the shortcomings of p-values (with the interactive piece) but I think the whole article is accessible to undergraduates, especially as it is littered with embedded links that provide greater information to previous research scandals and debates.