Reading a statistical table is like reading those BAL tables your Uni passes out during Orientation Week.

Alright. Stick with me. I had this idea when I was scrunching my hair this morning in a hotel in Long Island while on Spring Break.

I'm sure this is also the situation that inspired Salk to create the polio vaccine.

So, stats tables. These are tricky to teach because we don't use them, right? Not as mid-level statisticians. The software computes a test statistic, looks up that statistic on the appropriate table, and then reports a p-value. But, simultaneously, the students need to understand what is going on "under the hood."

This is a thing that always catches me up in class. Given how we do statistics nowadays, I spend all this freaking time explaining something of very little real-world value. Sorry, Fisher! Sometimes it feels like I'm trying to teach them how to decode something. But I may have thought of an easier way to explain it. While scrunching.

ANYWAY. I was scrunching my hair, and I thought, "Oh, test statistics tables (F, t, X2) are like those little business cards they gave us at Penn State during move in Freshman year." So we wouldn't kill ourselves by drinking. Note: When I was at PSU from 1997-2001, the local bars started to put big, permanent marker Xs on the hands of anyone celebrating their 21st birthday so they couldn't order 21 shots. And they had to do that because a girl who tried to do 21 shots on her 21st birthday had her life saved by the local hospital. Anyway.

See below:

Get it? Go Blue? Like PSU's battle cry? Anyone?

Here is a simpler version of the same thing, but just for the ladies:

So, you have to look up a thing along the top column and the first row and find your desired bit of information (BAL) in the table. Similarly, we use a t-table by looking up something in the top column and first row and finding the desired information. Dig it?

OH, SNAP!! You can use this with the William Gosset/Guinness Brewery stats lesson because Gosset created the first table.

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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