Hickey's "The 20 Most Extreme Cases Of ‘The Book Was Better Than The Movie"

Data has been used to learn a bit more about the age old observation that books are always better than the movies they inspire.

Fivethirtyeight writer Walk Hickey gets down to the brass tacks of this relationship by exploring linear relationships between book ratings and movie ratings.

The biggest discrepancies between movie and book ratings were for "meh" books made into beloved movies (see "Apocalypse Now").

How to use in class:

-Hickey goes into detail about his methodology and use of archival data. The movie ratings came from Metacritic, the book ratings came for Goodreads.
-He cites previous research that cautions against putting too much weight into Metacritic and Good reads. Have your students discuss the fact that Metacritic data is coming from professional movie reviewers and Goodreads ratings can be created by anyone. How might this effect ratings?
-He transforms his data into z-scores.
-The films that have the biggest movie:book rating discrepancies also serve as good examples of influential observations in linear relationships. How might such outliers effect the accuracy of the regression line predicted by this data?
-He does bring up the fact that this is a truncated data set: All of the stories that are included are books that garnered enough attention to be made into a movie.

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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