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Pizzagate gets even more ridiculous: “Either they did not read their own previous pizza buffet study, or they do not consider it to be part of the literature . . . in the later study they again found the exact opposite, but did not comment on the discrepancy.”

Background Several months ago, Jordan Anaya​, Tim van der Zee, and Nick Brown reported that they’d uncovered 150 errors in 4 papers published by Brian Wansink, a Cornell University business school professor and who describes himself as a “world-renowned eating behavior expert for over 25 years.” 150 errors is pretty bad! I make mistakes myself […]

7th graders trained to avoid Pizzagate-style data exploration—but is the training too rigid?

[cat picture] Laura Kapitula writes: I wanted to share a cute story that gave me a bit of hope. My daughter who is in 7th grade was doing her science project. She had designed an experiment comparing lemon batteries to potato batteries, a 2×4 design with lemons or potatoes as one factor and number of […]

Pizzagate update! Response from the Cornell University Media Relations Office

[cat picture] Hey! A few days ago I received an email from the Cornell University Media Relations Office. As I reported in this space, I responded as follows: Dear Cornell University Media Relations Office: Thank you for pointing me to these two statements. Unfortunately I fear that you are minimizing the problem. You write, “while […]

Clarke’s Law: Any sufficiently crappy research is indistinguishable from fraud (Pizzagate edition)

[cat picture] This recent Pizzagate post by Nick Brown reminds me of our discussion of Clarke’s Law last year. P.S. I watched a couple more episodes of Game of Thrones on the plane the other day. It was pretty good! And so I continue to think this watching GoT is more valuable than writing error-ridden […]

Division of labor and a Pizzagate solution

[cat picture] I firmly believe that the general principles of social science can improve our understanding of the world. Today I want to talk about two principles—division of labor from economics, and roles from sociology—and their relevance to the Pizzagate scandal involving Brian Wansink, the Cornell University business school professor and self-described “world-renowned eating behavior […]

Pizzagate and Kahneman, two great flavors etc.

[cat picture] 1. The pizzagate story (of Brian Wansink, the Cornell University business school professor and self-described “world-renowned eating behavior expert for over 25 years”) keeps developing. Last week someone forwarded me an email from the deputy dean of the Cornell business school regarding concerns about some of Wansink’s work. This person asked me to […]

Pizzagate update: Don’t try the same trick twice or people might notice

[cat picture] I’m getting a bit sick of this one already (hence image above; also see review here from Jesse Singal) but there are a couple of interesting issues that arose in recent updates.

Pizzagate, or the curious incident of the researcher in response to people pointing out 150 errors in four of his papers

There are a bunch of things about this story that just don’t make a lot of sense to me. For those who haven’t been following the blog recently, here’s the quick backstory: Brian Wansink is a Cornell University business school professor and self-described “world-renowned eating behavior expert for over 25 years.” It’s come out that […]

Consider seniority of authors when criticizing published work?

Carol Nickerson writes: I’ve written my fair share of letters to the editor and commentaries over the years, most of them languishing in the file drawer. It used to be impossible to get them published. The situation has improved a bit, but not enough. In any case, I never think about the sex of the […]

Best correction ever: “Unfortunately, the correct values are impossible to establish, since the raw data could not be retrieved.”

Commenter Erik Arnesen points to this: Several errors and omissions occurred in the reporting of research and data in our paper: “How Descriptive Food Names Bias Sensory Perceptions in Restaurants,” Food Quality and Preference (2005) . . . The dog ate my data. Damn gremlins. I hate when that happens. As the saying goes, “Each […]

On deck through the rest of the year (and a few to begin 2018)

Here they are. I love seeing all the titles lined up in one place; it’s like a big beautiful poem about statistics: After Peptidegate, a proposed new slogan for PPNAS. And, as a bonus, a fun little graphics project. “Developers Who Use Spaces Make More Money Than Those Who Use Tabs” Question about the secret […]

“Bombshell” statistical evidence for research misconduct, and what to do about it?

Someone pointed me to this post by Nick Brown discussing a recent article by John Carlisle regarding scientific misconduct. Here’s Brown: [Carlisle] claims that he has found statistical evidence that a surprisingly high proportion of randomised controlled trials (RCTs) contain data patterns that cannot have arisen by chance. . . . the implication is that […]

Another serious error in my published work!

Uh oh, I’m starting to feel like that pizzagate guy . . . Here’s the background. When I talk about my serious published errors, I talk about my false theorem, I talk about my empirical analysis that was invalidated by miscoded data, I talk my election maps whose flaws were pointed out by an angry […]

Honesty and transparency are not enough

[cat picture] From a recent article, Honesty and transparency are not enough: This point . . . is important for two reasons. First, consider the practical consequences for a researcher who eagerly accepts the message of ethical and practical values of sharing and openness, but does not learn about the importance of data quality. He […]

Reputational incentives and post-publication review: two (partial) solutions to the misinformation problem

So. There are erroneous analyses published in scientific journals and in the news. Here I’m not talking not about outright propaganda, but about mistakes that happen to coincide with the preconceptions of their authors. We’ve seen lots of examples. Here are just a few: – Political scientist Larry Bartels is committed to a model of […]

Organizations that defend junk science are pitiful suckers get conned and conned again

[cat picture] So. Cornell stands behind Wansink, and Ohio State stands behind Croce. George Mason University bestows honors on Weggy. Penn State trustee disses “so-called victims.” Local religious leaders aggressively defend child abusers in their communities. And we all remember how long it took for Duke University to close the door on Dr. Anil Potti. […]

Beyond subjective and objective in statistics: my talk with Christian Hennig tomorrow (Wed) 5pm in London

Christian Hennig and I write: Decisions in statistical data analysis are often justified, criticized, or avoided using concepts of objectivity and subjectivity. We argue that the words “objective” and “subjective” in statistics discourse are used in a mostly unhelpful way, and we propose to replace each of them with broader collections of attributes, with objectivity […]

Molyneux expresses skepticism on hot hand

image Guy Molyneux writes: I saw your latest post on the hot hand too late to contribute to the discussion there. While I don’t disagree with your critique of Gilovich and his reluctance to acknowledge past errors, I do think you underestimate the power of the evidence against a meaningful hot hand effect in sports. […]

Dear Cornell University Public Relations Office

image I received the following email, which was not addressed to me personally: From: ** Date: Wednesday, April 5, 2017 at 9:42 AM To: “gelman@stat.columbia.edu” Cc: ** Subject: Information regarding research by Professor Brian Wansink I know you have been following this issue, and I thought you might be interested in new information posted today […]

“Scalable Bayesian Inference with Hamiltonian Monte Carlo” (Michael Betancourt’s talk this Thurs at Columbia)

Scalable Bayesian Inference with Hamiltonian Monte Carlo Despite the promise of big data, inferences are often limited not by sample size but rather by systematic effects. Only by carefully modeling these effects can we take full advantage of the data—big data must be complemented with big models and the algorithms that can fit them. One […]