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...
One way of teaching about margin of error/confidence intervals is via political polling data. From mvbarer.blogspot.com Here is a good site that has a break down of polling data taken in September 2012 for the 2012 US presidential election. I like this example because it draws on data from several well-reputed polling sites, includes their point estimates of the mean and their margin of errors. This allows for several good examples: a) the point estimates for the various polling organization all differ slightly (illustrating sampling error), b) the margin of errors are provided, and c) it can be used to demonstrate how CIs can overlap, hence, muddying our ability to predict outcomes from point estimates of the mean. I tend to follow the previous example with this gorgeous polling data from Mullenberg College : This is how sampling is done, son! While stats teachers frequently discuss error reduction via big n , Mullenberg takes it a step further by o...