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Summary for those who are not deep into academia… Dan Ariely is a fraud. To be specific: he has been credibly accused of fabricating data in at least four sep

Summary for those who are not deep into academia… Dan Ariely is a fraud. To be specific: he has been credibly accused of fabricating data in at least four separate studies (Ten Commandments, delta dental, Hartford car insurance, and the study which he claimed Rossi ran for him which is discussed at the link). There are likely more. More broadly: There was a “golden age” for this kind of “behavioral economics”/social psychology work in the early 2000’s up to probably 2012-2015. Researchers would generally publish “results” which were either “delightfully counterintuitive” (ex: prime subjects to think about Florida and they walk slowly) OR featured extremely cheap interventions which showed large effects on outcomes (ex: signing an honest pledge at the top or the bottom of the page made a big difference in how honest people actually were on their tasks). In retrospect, there are two problems (at least!) with this literature. 1. There was no theory backing up any of these predictions, so you could rationalize any statistically significant result with a compelling story. Publish it and you’re on your way to tenure. 2. Related to (1.), you didn’t even need to bother running actual experiments at all! Just fabricate the data. Make it fit your cute story and you’re done. There are many tenured people in business schools (generally in “organizational behavior” or “social psychology” style departments rather than Econ) who were tenured on the basis of papers published under (1.) or (2.). This is a big shame because there are serious researchers who do difficult work in behavioral and experimental economics which is _not at all_ like this BS. (Some are personal friends of mine, so this bothers me.). There was no incentive for about 15 years to do the hard work though. And given that tenure is for life, it’ll be a long long time before these people are flushed out (barring more investigations, which should be encouraged!). What should you do? - be extremely, extremely skeptical of any results in the “priming” literature (eg the “think of Florida” example above). - be moderately skeptical of results in the “nudges” literature. The retirement savings stuff makes sense and has replicated but the honesty nudge above is BS and has not. - don’t get really excited about a researcher who says “economics bad - people dumb! - I behavioralist!” More than a few of these people have been revealed to be total frauds. - don’t click or share this stuff when you see it on Reddit, HN or anywhere else. That kind of engagement “feeds the beast” - Deans love it and Deans assume you haven’t fabricated your data. - maybe if you are specifically a Duke or Duke Fuqua alum send them a note asking why they aren’t taking the Ariely misconduct more seriously??? - (edit) throw away your copies of any of Ariely’s books. They are BS.

crazygringo 3y ago
Just to be clear, are the "credible" charges that he intentionally fabricated data, or that he received data (from assistants/corporations) that was fabricated, but that he didn't catch? Because he wasn't actually collecting the data directly himself, was he? And there's a world of difference between these two things.
- huitzitziltzin 3y ago
  You are correct that there’s a world of difference between those two accusations. Specific example: In the car insurance paper, which was covered by Data Colada, there was agreement among all the four authors that Ariely himself was the only person to touch the data. That data (IIRC) was completely fabricated. I’m happy to edit this comment and my original one if those details are wrong. I’ll give Ariely a presumption of innocence which he does not deserve and indeed which I suspect he abuses in research integrity investigations. (Edit) data colada link: http://datacolada.org/98 http://datacolada.org/98 At the end under “replies” Dan says himself that he was the person in touch with the insurer. It really beggars belief to imagine that there was someone at the insurer who fabricated a dataset which perfectly tells the BS story of this paper. Also note that another of the four authors (Gino) will soon be fired from HBS for fabricating data in a series of studies.
  - mustacheemperor 3y ago
    >First, it is visually and statistically (p=.84) indistinguishable from a uniform distribution ranging from 0 miles to 50,000 miles [5]. Think about what that means. Between Time 1 and Time 2, just as many people drove 40,000 miles as drove 20,000 as drove 10,000 as drove 1,000 as drove 500 miles, etc. [6]. This is not what real data look like, and we can’t think of a plausible benign explanation for it. It is just remarkable to see how people with enough knowledge of datascience to perpetrate this fraud for so long would also make errors as rudimentary as producing a faked 'random' data set in a uniform distribution. And with zero cars reporting over 50k miles driven. (edit: and so many others...this is worth reading just to see it all, thanks for linking). These frauds really are not as smart as they think they are. But I guess selling best-selling books about stuff you made up could make you believe you're a genius.

crazygringo 3y ago

Just to be clear, are the "credible" charges that he intentionally fabricated data, or that he received data (from assistants/corporations) that was fabricated, but that he didn't catch? Because he wasn't actually collecting the data directly himself, was he? And there's a world of difference between these two things.

huitzitziltzin 3y ago
You are correct that there’s a world of difference between those two accusations. Specific example: In the car insurance paper, which was covered by Data Colada, there was agreement among all the four authors that Ariely himself was the only person to touch the data. That data (IIRC) was completely fabricated. I’m happy to edit this comment and my original one if those details are wrong. I’ll give Ariely a presumption of innocence which he does not deserve and indeed which I suspect he abuses in research integrity investigations. (Edit) data colada link: http://datacolada.org/98 http://datacolada.org/98 At the end under “replies” Dan says himself that he was the person in touch with the insurer. It really beggars belief to imagine that there was someone at the insurer who fabricated a dataset which perfectly tells the BS story of this paper. Also note that another of the four authors (Gino) will soon be fired from HBS for fabricating data in a series of studies.
- mustacheemperor 3y ago
  >First, it is visually and statistically (p=.84) indistinguishable from a uniform distribution ranging from 0 miles to 50,000 miles [5]. Think about what that means. Between Time 1 and Time 2, just as many people drove 40,000 miles as drove 20,000 as drove 10,000 as drove 1,000 as drove 500 miles, etc. [6]. This is not what real data look like, and we can’t think of a plausible benign explanation for it. It is just remarkable to see how people with enough knowledge of datascience to perpetrate this fraud for so long would also make errors as rudimentary as producing a faked 'random' data set in a uniform distribution. And with zero cars reporting over 50k miles driven. (edit: and so many others...this is worth reading just to see it all, thanks for linking). These frauds really are not as smart as they think they are. But I guess selling best-selling books about stuff you made up could make you believe you're a genius.