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...
Nathan Yau, writing for Flowing Data, provides a good example of correlation, median, and correlation not equaling causation in his story, "Divorce and Occupation".
Yau looked at the relationship between occupation and divorce in a few ways.
He used one of variation upon the violin plot to illustrate how each occupation's divorce rate falls around the median divorce rate. Who has the lowest rate? Actuaries. They really do know how to mitigate risk. You could also discuss why median divorce rate is provided instead of mean divorce rate. Again, the actuaries deserve attention as they probably would throw off the mean.
Both scatter plots, when viewed at the website, are interactive. By cursoring over any dot, you can see the actual x- and y-axis data for that point.
Also, if you are teaching more advanced students, Yau shares some information on how he created these scatter plots at the end of the article.
Finally, talk to your students about the Third Variable Problem and how correlation does not equal causation. What is causing the relationship between income and divorce? Is it just money? Is it the sort of hours that people work? How does IQ figure into divorce? Maybe it has something to do with the fact that people who seek advanced degrees tend to get married later in life.
Yau looked at the relationship between occupation and divorce in a few ways.
He used one of variation upon the violin plot to illustrate how each occupation's divorce rate falls around the median divorce rate. Who has the lowest rate? Actuaries. They really do know how to mitigate risk. You could also discuss why median divorce rate is provided instead of mean divorce rate. Again, the actuaries deserve attention as they probably would throw off the mean.
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| https://flowingdata.com/2017/07/25/divorce-and-occupation/ |
He also looked at how salary was related to divorce, and this can be used as a good example of a linear relationship: The more money you make, the lower your chances for divorce. And an intuitive exception to that trend? Clergy members.
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| https://flowingdata.com/2017/07/25/divorce-and-occupation/ |
Also, if you are teaching more advanced students, Yau shares some information on how he created these scatter plots at the end of the article.
Finally, talk to your students about the Third Variable Problem and how correlation does not equal causation. What is causing the relationship between income and divorce? Is it just money? Is it the sort of hours that people work? How does IQ figure into divorce? Maybe it has something to do with the fact that people who seek advanced degrees tend to get married later in life.