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Showing posts with the label JAMA

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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...
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A good JAMA article that demonstrates how to appropriately share relative and absolute risks.

TL:DR: Sugary drinks might up your risk for oral cavity cancer (so says relative risk) but probably won't be the thing that kills you (so says absolute risk).  In depth: I love teaching applied statistics, including showing my students how to identify and properly attention-grabbing examples of relative risk ( 1 , 2 ). HOWEVER, relative and absolute risk aren't lying. But they can scare people, so I think it is important to share both calmly.  This example from JAMA Otolaryngology is a good example of how to responsibly share relative and absolute risk. It has a very calm, non-click bait article title:  https://jamanetwork.com/journals/jamaotolaryngology/fullarticle/2831121 Cool. Also, thanks for using female research participants. Next, the results are described in a non-salacious manner, with the absolute risk in red and relative risk in blue. How could you use this in class? Sharing relative risk isn't in and of itself unethical. Using it to scare people is questi...
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Chris Taylor's "No, there's nothing wrong with your Fitbit"

Taylor, writing for Mashable , describes what happens when carefully conducted public health research (published in the  Journal of the American Medical Association ) becomes attention grabbing and poorly represented click bait. Data published in JAMA (Case, Burwick, Volpp, & Patel, 2015) tested the step-counting reliability of various wearable fitness tracking devices and smart phone apps (see the data below). In addition to checking the reliability of various devices, the article makes an argument that, from a public health perspective, lots of people have smart phones but not nearly as many people have fitness trackers. So, a way to encourage wellness may be to encourage people to use the the fitness capacities within their smart phone (easier and cheaper than buying a fitness tracker). The authors never argue that fitness trackers are bad, just that 1) some are more reliable than others and 2) the easiest way to get people to engage in more mindful walking...
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