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Updated example of using baby names to illustrate bimodal distributions.

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's "You Can’t Trust What You Read About Nutrition"


Fivethirtyeight provides lots of beautiful pictures of spurious correlations found by their own in-house study.
At the heart of this article are the limitations of a major tool use in nutritional research, the Food Frequency Questionnaire (FFQ). The author does a mini-study, enlisting the help of several co-workers and fivethirtyeight.com readers. They track track their own food for a week and reflect on how difficult it is to properly estimate and recall food (perhaps a mini-experiment you could do with your own students?).

And she shares the spurious correlations she found in her own mini-research:



Aschwanden also discusses how much noise and lack of consensus their is in real, published nutritional research (a good argument for why we need replication!): 

http://fivethirtyeight.com/features/you-cant-trust-what-you-read-about-nutrition/

How to use in class:
-Short comings of survey research, especially survey research that relies on accurate memories
-Spurious correlations (and p-values!)
-Correlation does not equal causation
-Why replication is necessary


Also included is an amusing video that shows what it is like to be a participant in a nutrition study. It details the FFQ, or Food Frequency Questionnaire. And the video touches on serving sizes and portions, how how it may be difficult for many of to properly estimate (per the example) how many cups of spare ribs we consume per week. 

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