Do Taylor Swift's album variants violate the assumption of independence?

No, that is not a dig at her romantic relationships.

It is instead a question about the impact of her numerous variants on descriptive data in the music industry. And if you found your way to this blog, you know that I love a good, relevant, pop-culture-driven example for explaining statistical concepts. Especially somewhat dry concepts, like the assumption of independence when collecting data.

Anyway.

Per Microsoft Copilot:

Across all formats, there are 34 different versions of the album:

8 vinyl
18 CD
1 cassette
7 digital

Microsoft Copilot. (2025, November 8). Response to query about Taylor Swift’s album variants [AI-generated response]. Microsoft Copilot.

A picture containing all eight vinyl variants of Taylor Swift's Life of a Show Girl

https://www.reddit.com/r/TrueSwifties/comments/1n04kdu/the_life_of_a_showgirl_vinyl_variants_announced/

When overall album sales are counted by major industry players (Billboard, Luminate), every variant sale counts as one sale.

So, she is selling many albums, but it raises the question of whether her sales truly reflect the album's popularity among music listeners because the variants violate the assumption of independence.

For example, I purchased one copy of Brandi Carlile's new album, Returning to Myself (I don't collect records, but my husband does, and Brandi Carlile is one of the artists who exists in the Venn diagram overlap of our tastes in music). If someone is trying to ascertain the overall popularity of albums in the US this month, my one "vote" for BC would be up against a TS super collector who owns 10 variants of the same album.

Which makes me think of the statistical assumption of independence. Observations in your data should be independent of one another. If multiple versions of the same album are purchased by one person, surely the record sale numbers are no longer an accurate indicator of record popularity. Due to the many variants, the assumption of independence is violated.

Not that anyone cares about this, or that I even care about this strongly, but this would be a good, relevant, timely example of one of the assumptions that underlie all of our statistical analyses. Also, TS's You're On Your Own, Kid, will always be my walk-up song, but this phenomenon makes me wonder about the purity of album sales charts and statistical assumptions.

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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