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.