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...
51% of the babies born in the US are male. Why? For a long time, people just assumed that the skew started at conception. Then Steven Orzack decided to test this assumption. He (and colleagues) collected sex data from abortions, miscarriages, live births (30 million records!), fertility clinics (140,00 embryos!), and different fetal screening tests (90,000 medical records!) to really get at the root of the sex skew/conception assumption. And the assumption didn't hold up: The sex ratio is pretty close to 50:50 at conception. Further analysis of the data found that female fetuses are more likely to be lost during pregnancy. Original research article here. Richard Harris' (reporting for NPR) radio story and interview with Orzack here.
Use this story in class as a discussion piece about long held (but never empirically supported) assumptions in the sciences and why we need to conduct research in order to test such assumptions. For example:
1) Discuss the weaknesses of previous attempts to answer the question of sex differences in birth rates.
2) Explain why samples matter/why sex selective abortion in two very large countries could skew this data and why it was important to use US/Canada data.
3) Discuss the fact that people kind of accepted previous explanation in lieu or proper research methods to answer this question.
4) I could see how you could use the basic premises in order to introduce the concepts behind chi square tests...Expected data: 50/50, Observed data: 51:49.
5) What further questions does this research raise (For example, male fetuses are especially vulnerable during the first trimester due to genetic abnormalities. But why do female fetuses become more vulnerable during the second trimester?).
Use this story in class as a discussion piece about long held (but never empirically supported) assumptions in the sciences and why we need to conduct research in order to test such assumptions. For example:
1) Discuss the weaknesses of previous attempts to answer the question of sex differences in birth rates.
2) Explain why samples matter/why sex selective abortion in two very large countries could skew this data and why it was important to use US/Canada data.
3) Discuss the fact that people kind of accepted previous explanation in lieu or proper research methods to answer this question.
4) I could see how you could use the basic premises in order to introduce the concepts behind chi square tests...Expected data: 50/50, Observed data: 51:49.
5) What further questions does this research raise (For example, male fetuses are especially vulnerable during the first trimester due to genetic abnormalities. But why do female fetuses become more vulnerable during the second trimester?).