Seven mini-stats lessons, crammed into nine minutes.

I found this Tweet, which leads to a brief report on BBC.

A recent report from the World Obesity Federation shows COVID death rates are higher in countries where more than half the population is overweight. Cause and effect, or bad statistics? @TimHarford and @d_spiegel explore - with some maths from me.

You can listen on @BBCSounds https://t.co/hevepmz8RC
— stuart mcdonald (@ActuaryByDay) March 14, 2021

The BBC has a show called "More or Less," and they explained a recent research finding connecting obesity to COVID 19 deaths. Here is the original research study. Here is a pop treatment of the original study. For more stats news, you can follow "More or Less" on Twitter.

And they cram, like, a half dozen lessons in this story. It is amazing. I've tried to highlight some of the topics touched upon in this story. How can you use it in class? I think it would be a good final exam question. You could have your students listen to the story, and highlight expand upon one of the fallacies. Or you could use this piece to introduce the bigger topic of science communication.

1) Correlation doesn't necessarily mean causation

2) Don't trust all data, and don't trust all media reporting of data

3) Interactions: There is an interaction between age and obesity, and they describe it quickly but clearly. Obesity makes your risk go up more when you are young than when you are old. It raises a 30-year-old's risk to a 45-year-old's risk.

Here is the link to the COVID calculator discussed in the piece: https://alama.org.uk/covid-19-medical-risk-assessment/

4) Turning continuous data into dichotomous data: So, the countries were coded as obese or not. Blerg

5) Base rates: The presentation of the data, using dichotomous "obese or not" data, and counting each country as a research participant, obscures some crucial facts that need to be accounted for when determining the overall impact of obesity:

a) What portion of the world's population is accounted for by each country? 2%? 10? If you are trying to argue that obesity was a driver in COVID deaths, your data needs to account for this. Since the original data looked at countries as their "participants," this is an issue that needs to be taken into account when understanding the overall impact of obesity.

b) Advanced age is a powerful predictor of COVID deaths. The data did not take into account demographic differences in these countries. Some countries have enormous elderly populations (and will, by that fact, have more people die of COVID if they catch COVID, regardless of obesity)

7) Covariants: At the end, they talk to an actuary about global deaths' if obesity is eliminated. I think this is a good example of co-variation. Overall, deaths would have been lowered by 7%.

One tiny caveat, Americans: They talk about stones and kilograms and stuff, so you might need to translate some of that for your students.

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