Wednesday, June 27, 2012

If Ariely is right there's a lot more fraud in science going on

Dan Ariely has a new book out. It comes highly recommended from me even though (better be 100% honest in a post about fraud]) I haven't read it. But I've seen him give the talk many times and I've read some book reviews.

The short version is that cheating in our society works like this: most people cheat a little bit, a few people cheat a lot.

Quantitatively, most of the problems in society, I assume he argues, come from the large amount of small cheating, rather than the Madoffs and Barry Bonds.

If Ariely is right, how does this affect our understanding of scientific fraud?

Well, most people get truly exercised about the Hausers and the Stepels of the world. They are the funnest to talk about, certainly. But what if they are the just tip of the iceberg?

Well, first, big fraudsters are potentially more important in science than in other fields because of the non-linear nature of scientific progress. Big names are attractor states, and if they are totally wrong, lots of resources can get redirected to them. Especially the most valuable resource, the minds of the next generation.

But of course, those big fraudsters also get more scrutiny and presumably get caught sooner. Their status is more precarious (Im assuming).

And what about the small fish?

First of all, it make me worry about any p between 0.04 and 0.05. I mentally replace it with a 0.06.

More generally, it makes me more worried about fields where p's close to those values are the norm. [In my field, p's tend to me lower than that, for whatever reason]. Those fields include animal psychology, for example. Also, medicine.

It also makes me worry about fields where scientists can run slightly different experiments quickly and easily and get slightly different data. So for example, when a scientist has to travel to Mauna Loa to gather some astronomical observations about a once-in-a-lifetime comet, it's hard for her to go replicate that experiment under slightly different conditions. Or to go new analyses, which is basically replicating the experiment once the data is collected.

If these people are interested in not getting caught, they are probably publishing results that generally agree with the broader paradigm. They are not sticking their necks out publishing counterintuitive findings. This creates a false sense of validation for the dominant view, makes it even harder to publish contradictory findings, and increases biases in the literature.

If cheating is pretty common, it also would explain why meeting people is so important in science. Having a personal relationship makes it easier to judge whether someone is one of those mild fraudsters or whether they are one of the truly upright people. Also, being friends with someone makes it easier to forgive them if one suspects they are cheating a little bit.

I don't have any conclusions or suggestions. This has just been on my mind lately. I just think of the posters at SFN and wonder if maybe half of them are slight frauds.


  1. I thought about this a lot recently too, with 2 professors in Holland resigning because of big fraud scandals. It's a pretty slippery slope in my opinion. For example if people show an example cell, trace or whatever, they always say that it's a representative example when actually it's probably the prettiest and best example that they've got. Everyone knows that and is okay with that kind of euphemism. I think it's important that PIs teach their students that they don't succumb to the pressure of publishing nice and pretty data and keep being critical and honest about their findings.

    1. Well the good news is, there's two more faculty slots opened in Holland for you and Brown Eyes, right?!

      I hope you are right about the value of teaching ethics. I personally am skeptical that pedagogy has any effect on ethical behavior. On the other hand, I feel like leading by example has some good effects down the line.

      As for example cells, I always try to emphasize in talks that they are cells chosen to illustrate the phenomenon observed in the population, rather than typical cells.

      Come to think of it, I don't do that as much as I should.

  2. You have a dangerously fucked up notion of what inferential statistics is telling us.


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