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 is pretty easy to follow. Life expectancy varies by income level. The story becomes a good example for a statistics class because in the interview, the researcher describes a multivariate model. One in which multiple different independent variables (drug use, medical insurance, smoking, income, etc.) could be used to explain the disparity the exists in lifespan between people with different incomes.
As such, this story could be used as an example of multivariate regression. And The Third Variable Problem. And why correlation isn't enough.
In particular, this part of the interview (between interviewer Ari Shapiro and researcher Gary Burtless) refers to the underlying data as well as the Third Variable Problem as well as the amount to variability that can be assigned to the independent variables he lists).
You could also talk about how to model this data to allow for moderators. The interviewer says that health insurance helps offset this disparity. But does it effect everyone equally? Perhaps number of cigarettes smoked per day moderates this relationship, such that health insurance narrows the disparity with non-smokers but doesn't assist smokers.
As such, this story could be used as an example of multivariate regression. And The Third Variable Problem. And why correlation isn't enough.
In particular, this part of the interview (between interviewer Ari Shapiro and researcher Gary Burtless) refers to the underlying data as well as the Third Variable Problem as well as the amount to variability that can be assigned to the independent variables he lists).
SHAPIRO: Why is this gap growing so quickly between life expectancy of rich and poor people?
BURTLESS: We don't know. More affluent Americans tend to engage more in systematic exercise. They are less likely to be obese. Their smoking rates are lower. Those differences can help account for why there is a difference in how long people live. However, they do not seem to account for more than about a fifth of the increase in mortality differences between affluent and less-affluent people. So something else is going on in the background. It could be that there are behaviors that we could not examine. None of our surveys that we have access to asked about people's use of illegal drugs. Possibly, it is simply the effect of growing income inequality over the course of people's lives.
I also like this example because it can be used to open your students minds up with how to study specific hypotheses after a result is known. Alright, a correlation has demonstrated this disparity. Why? How can we study this better? The author presents a list of factors that have already been examined. What else could there be?BURTLESS: We don't know. More affluent Americans tend to engage more in systematic exercise. They are less likely to be obese. Their smoking rates are lower. Those differences can help account for why there is a difference in how long people live. However, they do not seem to account for more than about a fifth of the increase in mortality differences between affluent and less-affluent people. So something else is going on in the background. It could be that there are behaviors that we could not examine. None of our surveys that we have access to asked about people's use of illegal drugs. Possibly, it is simply the effect of growing income inequality over the course of people's lives.
You could also talk about how to model this data to allow for moderators. The interviewer says that health insurance helps offset this disparity. But does it effect everyone equally? Perhaps number of cigarettes smoked per day moderates this relationship, such that health insurance narrows the disparity with non-smokers but doesn't assist smokers.