Quealy & Sanger-Katz's "Is Sushi ‘Healthy’? What About Granola? Where Americans and Nutritionists Disagree"

UPDATE, 9/22/22: Here is a non-paywalled link to this information: https://www.nytimes.com/2017/10/09/learning/whats-going-on-in-this-graph-oct-10-2017.html

This article from the NYT is based on a survey. That survey asked a bunch of nutritionists if they considered certain foods healthy. Then they asked a bunch of everyday folks if they considered the same foods to be healthy.

Then they generated the percentage of each group that considered the food healthy. And the NYT put the nutritionist responses on a Y-axis, and commoners on the X, and made a lovely scatterplot...

Nutritionists and non-nutritionists agree that chocolate chip cookies are not healthy. However, nutritionists are far more critical of American cheese than are non-nutritionists.

...and provided us with the raw data as well.

The article mostly highlights the foods where there is a large discrepancy of opinion between the two groups (see above).

There are many teachable moments in this article:

-Correlation. Obvs.
-Inter-rater reliability. Kind of. It is high when nutritionists and non-nutritionists agree, and low when they don't. When there is low reliability, the food becomes an outlier (so talk about outliers/influential observations).
-Lots of descriptive data is presented.
-If you wanted, you could conduct a paired t-test on this data. Compare nutritionist data to layperson data. This data is available in separate tables accompanying the story.
-While you are at it, run the correlation and generate the regression line.
-Discuss real-life application of this data. What are the commonalities for food that laypeople ID as unhealthy that actually are healthy? And vice-versa? How can the government concentrate on suggesting healthy, confusing food to laypeople?

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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