Our World in Data's deep dive into human height. Examples abound.

Stats nerds: I'm warning your right now. This website is a rabbit hole for us, what with the interactive, customizable data visualizations. Please don't click on the links below if you need to grade or be with your kids or drive.

At a recent conference presentation, I was asked where non-Americans can find examples like the ones I share on my blog. I had a few ideas (data analytic firms located in other countries, data collected by the government), but wanted more from my answer.

BUT...I recently discovered this interactive from Our World in Data. It visualizes international data on human height, y'all with so many different examples throughout. I know height data isn't the sexiest data, but your students can follow these examples, they can be used in a variety of different lessons, and you can download all of the data from the beautiful interactive charts.

1. Regressions can't predict forever. Trends plateau.

I'm using this graph to as an example of how a regression equation can't predict forever, and that trends plateu. This example depicts human height. There is also a section of this website dedicated to why this may be happening.

https://ourworldindata.org/human-height#height-is-normally-distributed

2. Within-group variability

Male height differs by country. Display whichever country you want. There are a bunch.

https://ourworldindata.org/human-height#increase-of-human-height-over-two-centuries

3. The empirical rule, normal curves, within-group vs. between-group differences

Normal curves for male and female heights.

https://ourworldindata.org/human-height#height-is-normally-distributed

5. Linear relationships

Around the globe, there is a positive relationship between mean male and mean female height. You can see the big picture in this scatterplot. You can also highlight individual countries, and download the data for analysis in class.

https://ourworldindata.org/human-height#did-heights-across-the-world-increase-more-for-men-or-women

6. Third Variable Problem

As male height increases, child mortality decreases. Are big tall dudes making kids live longer? That seems unlikely. Challenge your students to find the third variable that likely causes both.

A graph depicting the negative relationship between child mortality and average male height.

https://ourworldindata.org/human-height#how-does-health-affect-height

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.

Tyler Vigen's Spurious Correlations

Tyler Vigen has has created a long list of easy-to-paste-into-a-powerpoint graphs that illustrate that correlation does not equal causation. For instance, while per capita consumption of cheese and number of people who die by become tangled in their bed sheets may have a strong relationship (r = 0.947091), no one is saying that cheese consumption leads to bed sheet-related death. Although, you could pose The Third Variable question to your students for some of these relationships). Property of Tyler Vigens, http://i.imgur.com/OfQYQW8.png Vigen has also provided a menu of frequently used variables (deaths by tripping, sunlight by state) to help you look for specific examples. This portion is interactive, as you and your students can generate your own graphs. Below, I generated a graph of marriage rates in Pennsylvania and consumption of high fructose corn syrup. Generated at http://www.tylervigen.com/

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