Interpreting effect sizes: An Olympic-sized metaphor

First, a pun:

American athlete Athing Mu broke the American record for the 800m. I guess you could say...that Mu is anything but average!! HAHAAAHAHAHHA.

Tweet featuring Athing Mu's record breaking 800m

https://twitter.com/Notawful/status/1409456926497423363

Anyway. It is late June 2021, and my Twitter feed is filled with amazing athletes qualifying for the Olympics. Athletes like Sydney McLaughlin.

Image of Sydney McLaughlin after qualifying for the Olympics

That picture was taken after McLaughlin a) qualified for the 2021 Olympics AND b) broke the 400m hurdle world record. Which is amazing.

Now, here is where I think we could explain effect size interpretation. How big was McLaughlin's lead over the previous record?

From SpectrumNews1

McLaughlin broke the world record by less than a second. But she broke the world record so less than a second is a huge deal. Similarly, we may have Cohen's small-medium-large recommendations when interpreting effect sizes, but we always need to interpret an effect size within context. Does a small effect size finding explain more variance than any previously proposed variable? Is the small effect size related to a very inexpensive and easy-to-administer health intervention? Is that medium effect size a sound replication of previous findings but not particularly novel? It all depends on the context, the research question, and the stakes of what you are studying. Similarly, Laughlin's less than second matters a lot in an elite track and field event, but probably not so much at a local 5K race.

When I teach students to interpret statistical tests, I explain NHST and effect sizes. I think it is important to emphasize the nuance of effect sizes to my junior statisticians.

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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