Why measures of variability matter: Average age of death in The Olden Days

Alright, this is a 30-second long example for a) bimodal distributions and b) why measures of variability matter when we are trying to understand a mean.

And that mean is...AGE OF DEATH.

A GIF of Wednesday Addams trying to smile. And failing.

My inspiration for this tweet is:

I’m just a girl, standing in front of the internet, asking it to understand that historical life expectancies doesn’t mean most people died at 45 but rather that infant mortality was super high and pulled down the average.
— Angelle Haney Gullett (@CityofAngelle) January 12, 2022

Gullett refers here to the commonly held belief that if the mean life span Back In The Day was 45, or thereabout, everyone was dying around 45. NOT SO.

Why?

The short answer is no. Broadly speaking, there were two choke point of human mortality. Younger than 5, and again around 50. If you made it through those, barring accidents, you likely had what was a normal lifespan of ~65-70 years.

And this is why I’m no fun at parties 😂
— Angelle Haney Gullett (@CityofAngelle) January 12, 2022

OK. Angelle is invited to my parties. She would tell us how many humans didn't survive childhood. AND THEN...a bunch of people would croak around 50.

*Let's take a moment and send some good thoughts and gratitude to the memories of Jonas Salk, Edward Jenner, and Alexander Flemming.*

Yet another Twitterer then shared this data visualization to demonstrate how mortality has changed over time.

Here is the data on that.

Yes, it was was extremely common that children died, as you say.

But that was not the only reason for low life expectancy. Mortality at higher ages was also very high. Much higher than today.

And so it was the case that exactly half were dead by 45. pic.twitter.com/ZK0REZQR57
— Max Roser (@MaxCRoser) January 14, 2022

When I shared this in class, I threw these Tweets up on a PPT slide. Again, it shows a roughly bimodal distribution, with death increasing in childhood and 50. It also demonstrates why we need to discuss the variability in a data set, not just the midpoint.

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