Evidence of Fraud in an Influential Study About Procrastination

Dan Ariely has a long history of controversies and studies with falsified data. He wrote 3 popular books and gained a lot of fame before others started uncovering the problems in his studies

https://en.wikipedia.org/wiki/Dan_Ariely

It’s amazing that problems with his studies are still being uncovered. Equally amazing that Duke still maintains a relationship with him after so many problems have been identified with his research.

It does seem like its way too easy to do this kind of fraud.

The study seems extremely easy to replicate (no fancy equipment needed, just ask some people to proofread), and yet nobody did until now. Is there some way to change incentives to get people to try and replicate? Assign undergrads to replicate random research papers?

I came here for a procrastination joke comment and didn't find it, but I suppose someone will get around to it eventually.

No paper should ever be referenced until it has been replicated by an independent group. The replication crisis in science is real, and it won't take long for no one to believe anything anymore.

Figures, even the studies about procrastination are fraught with issues. Guess I'll put off reading the details until tomorrow.

One thing I didn't know before reading this: an unusually large effect size can itself be a red flag. You don't have to understand all the statistics to at least notice when a result looks almost too good to be true.

I'm glad the authors seem to have acknowledged the problem and not doing much about it :-)

Having skimmed this article and the replication study itself (which references some studies that complicate our common understanding of the positive effects of deadlines) I’m left wondering what the constructive alternative is to deadlines and/or how deadlines should be maintained or determined.

Short of blindly outsourcing the work to an LLM and checking to see if its hallucinated problems are actually problems[0], how can more laymen (without the ability to run replication studies, and without degrees in statistics) learn to more easily identify questionable/potentially fraudulent research like this?

This is a question that has been really bothering me for a while. I have a short list of things I’ve learnt to check, but it’s all stuff I’ve picked up in a very ad hoc way (reading and listening to these kinds of critical analyses, mostly). For example, until now, I wouldn’t think about implausibly large Cohen’s d, despite this seeming like an easily generalisable rule that anyone could eyeball. But without being told, and without the reference points given in the article, I wouldn’t have a clue. And I would like to have more of a clue. And I would like other people to have more of a clue. How?

[0] https://news.ycombinator.com/item?id=49484925

They probably procrastinated all their deadlines and then fibbed things.

Dan Ariely remaining at Duke, after it has become clear that much if not most of his work is fraudulent, is at least as big a black mark for Duke as Jason Arday was for Cambridge.

Not to mention the Epstein ties.

The reason why people(or ai agents) have not been able to systematically destroy bad research in fields where fraud is so rampant is because they data are not published or easily accessible without emailing the author and asking them.

I sincerely hope that any self respectful journal that publishes papers should have a minimum bar: submit the data on a public link with no paywall or blockers and feature the link prominently on your paper.

And yet it will live forever, just like Dunning-Kruger despite its statistical issues. I fully expect to see it cited during some workshop or other pointless training exercise.

The name of the place should be enough of a giveaway for any thinking person Center for Advanced Hindsight.

SMH.

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Oh the irony...