The Economist: Election predictions, confidence intervals, and measures of central tedency.

The Economist created interactive visualizations for various polling data related to the 2020 U.S. Presidential election.

While illustrating this data, they used different measures of central tendency and different confidence intervals. Like, it is one thing to say that Candidate A is polling at 47% with a margin of error of 3.2%. I think it is much more useful to illustrate what the CI is telling us about the likely true parameter, based on what we have collected from our imperfect sample. The overlap in confidence intervals when polling is essential to understanding polling.

How to use in class:

1) Electoral college predictions, illustrated with median, 60%, and 95% confidence intervals.

Also, I like how this illustrates the trade-off between precision and the size of a confidence interval. The 60% CI is more narrow, but you are only 60% confident that it contains the true number of electoral college votes. Meanwhile, the 95% confidence interval is much wide but also more likely to contain the true portion of votes.

2). Popular vote illustrated with mean and a 95% confidence intervals:

3) I think these visualizations do a great job of really emphasizing what CIs give us: In an unpredictable world, they provide space in which our best guess of the truth.

4) Data visualization example of how to visualize a measure of central tendency and confidence interval with repeated measure data.

5) There are multiple ways to gather data about an underlying question. The question here: Who will win the 2020 US presidential election? Here, popular vote and electoral college votes are both taken into consideration when weighing chances.

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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