John Bohannon's "I fooled millions into thinking chocolate helps weight loss. Here's how."

http://io9.com/i-fooled-millions-into-thinking-chocolate-helps-weight-1707251800

This story demonstrates how easy it is to do crap science, get it published in a pay-to-play journal, and market your research (to a global audience). Within this story, there are some good examples of Type I error, p-hacking, sensationalist science reporting, and, frankly, our obsession with weight and fitness and easy fixes—also, chocolate.

Here is the original story, as told to io9.com by the perpetrator of this very conscientious fraud, John Bohannon. Bohannon ran this con to expose just how open to corruption and manipulation the whole research publication process can be (BioMed Central scandal, for another example), especially when it just the kind of research that is bound to get a lot of media attention (LaCour scandal, for another example).

Bohannon set out to "demonstrate" that dark chocolate can contribute to weight loss. He ran an actual study (n = 26). He went on a fishing expedition and measured 18 different markers of health. He did find a significant relationship between chocolate and lower cholesterol (a good example of likely Type I error).

So a manuscript was created. Bohannon describes how quickly their manuscript was accepted at several pay to play journals (no peer review, either, opening up a class discussion about both peer review as well as the gradient of pay to play journals, some of which are peer-reviewed, many of which are not).

Dr. Bonahon then describes how he created a website called "The Institute of Diet and Health" to legitimize his research (to be clear, this Institute does not exist) as well as a press release for his study. Then, the media did his work for him. Once one outlet picked up his story, so did hundreds of others. One glimmer of hope: While the media just ran with this story, Bohannon states that internet discussion boards associated with the different media outlets actually yielded intelligent discussions that picked apart the flaws of the study.

So, I think that the whole io9.com piece would be a good reading assignment for a statistics or research methods class. Additionally, if you are looking to use this story in class, here is an NPR interview with Bohannon.

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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