Aschwanden's "Why We Still Don’t Know How Many NFL Players Have CTE"

This story by Christine Aschwanden from 538.com describes the limitations of a JAMA article. That JAMA article describes a research project that found signs of Chronic Traumatic Encephalopathy (CTE) in 110 out of 111 brains of former football players.

How to use in stats and research methods:
1) It is research, y'all.
2) One of the big limitations of this paper comes from sampling.
3) The 538 article includes a number of thought experiments that grapple with the sampling distribution for all possible football players.
4) Possible measurement errors in CTE detection.
5) Discussion of replication using a longitudinal design and a control group.

The research:

The JAMA article details a study of 111 brains donated by the families deceased football players. They found evidence of CTE in 110 of the brains.

Which sounds terrifying if you are a current football player, right? But does this actually mean that 110 out of 111 football players will develop CTE? Not necessarily, if you consider sampling bias and the sampling distribution of the sample mean.

Is this sample a random sample of football players? Why do you think these 111 former football players/their next of kin went out of their way to donate their brains to science? Were these men experiencing symptoms prior to death? From Aschwanden:

As such, we really need to consider this sample within the bigger idea of all possible samples that could be generated from the true population of football players. This article never, ever refers to the sampling distribution by name, but does write out some interesting thought experiments that are referring to the concept of the sampling distribution.

Beyond sampling bias, how are we diagnosing CTE? Some criticism has been levied at the original paper for being to lax in diagnosing CTE. This could be a good way to discuss operationalizing and measuring any dependent variable.

Finally, there is nothing like a good control group or longitudinal research design to truly establish a causal relationship:

Comments

Ways to use funny meme scales in your stats classes

Have you ever heard of the theory that there are multiple people worldwide thinking about the same novel thing at the same time? It is the multiple discovery hypothesis of invention . Like, multiple great minds around the world were working on calculus at the same time. Well, I think a bunch of super-duper psychology professors were all thinking about scale memes and pedagogy at the same time. Clearly, this is just as impressive as calculus. Who were some of these great minds? 1) Dr. Molly Metz maintains a curated list of hilarious "How you doing?" scales. 2) Dr. Esther Lindenström posted about using these scales as student check-ins. 3) I was working on a blog post about using such scales to teach the basics of variables. So, I decided to create a post about three ways to use these scales in your stats classes: 1) Teaching the basics of variables. 2) Nominal vs. ordinal scales. 3) Daily check-in with your students. 1. Teach your students the basics...

Leo DiCaprio Romantic Age Gap Data: UPDATE

Does anyone else teach correlation and regression together at the end of the semester? Here is a treat for you: Updated data on Leonardo DiCaprio, his age, and his romantic partner's age when they started dating. A few years ago, there was a dust-up when a clever Redditor r/TrustLittleBrother realized that DiCaprio had never dated anyone over 25. I blogged about this when it happened. But the old data was from 2022. Inspired by this sleuthing, I created a wee data set, including up-to-date information on his current relationship with Vittoria Ceretti, so your students can suss out the patterns that exist in this data.

If your students get the joke, they get statistics.

Gleaned from multiple sources (FB, Pinterest, Twitter, none of these belong to me, etc.). Remember, if your students can explain why a stats funny is funny, they are demonstrating statistical knowledge. I like to ask students to explain the humor in such examples for extra credit points (see below for an example from my FA14 final exam). Using xkcd.com for bonus points/assessing if students understand that correlation =/= causation What are the numerical thresholds for probability? How does this refer to alpha? What type of error is being described, Type I or Type II? What measure of central tendency is being described? Dilbert: http://search.dilbert.com/comic/Kill%20Anyone Sampling, CLT http://foulmouthedbaker.com/2013/10/03/graphs-belong-on-cakes/ Because control vs. sample, standard deviations, normal curves. Also,"skewed" pun. If you go to the original website , the story behind this cakes has to do w...

Search This Blog