Psychedelics research: A blog post with Beth Morling

Now and again, I run across a news article or psychological question that is so big that it bleeds out of straight statistics and requires a thorough understanding of the research methodology that guides statistical choices.

When that happens, I email my buddy and fellow W.W. Norton author, Beth Morling, and we write a joint blog post.

Recently, I emailed her because research on using psychedelics to treat many different mental disorders has been in the news. President Trump fast-tracked this research, and the Journal for the American Medical Association recently published a big meta-analysis on the topic.

Psychedelic research has always interested me because of psychology, but it has always amused me because of how you run a proper double-blind research study if your experimental participants KNOW that they are hallucinating and your control group participants know they are not?

This broader question offers a few great discussion options for you and your students as you work through it.

In our co-authored blog post, Beth got into the nitty-gritty of how researchers create comparable control groups to work around this unique research problem. For my part, I wrote about the statistics for comparing two groups (high vs. not-high), as well as participants in the control group versus microdosing versus large-dose. Also, as you will learn, one control group used in this research is people on antidepressants. Which take a while to kick in. So how do you study multiple groups at multiple times?

If this idea was helpful, you'll probably enjoy my textbook, Psychological Statistics for Everyone. It's basically this blog, but organized into a semester.

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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