Brian Nosek
speaker
65 appearances
2 recordings
1 series
first heard Dec 2024
last heard Jan 2025
Brian Nosek’s voice in public audio — every appearance, attributed to the second.
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Fraud has existed since science has existed. And that's primarily because humans are doing the science. And people come with ideas, beliefs, motivations, reasons that they're doing the research that they do. And in some cases, people are so motivated to advance an idea or themselves that they are willing to change the evidence fraudulently to advance that idea or themselves.
Our funders include NIH, NSF, NASA, and DARPA as federal sources, and then a variety of private sources such as the John Templeton Foundation, Arnold Foundation, and many others. And that diverse group of funders – and it's quite diverse – I think share the recognition that the substantive things that they are trying to solve –
won't be solved very effectively if the work itself is not done credibly.
There are specific cases where a finding gets translated into public policy or into some type of activity that then ends up actually damaging people, lives, treatments, solutions. One of the most prominent examples is the Wakefield scandal relating to development of autism and the notion that vaccines might contribute to
And that has had an incredibly corrosive impact on public health, on people's beliefs about the sources of autism, the impacts of vaccines, et cetera. And that is very costly for the world. There's also a local cost in academic research, which is just a ton of waste.
So even if it doesn't have public downstream consequences, if a false idea is in the literature and other people are trying to build on it, it's just waste, waste, waste, waste.
Yeah, I have always had an interest in how to do good science as a principled matter. And in doing that, we in the lab would work on developing tools and resources to be more transparent with our work, to try to be more rigorous with our work, to try to do higher powered research. more sensitive research designs.
And so I wrote grant applications to say, can we make a repository where people can share their data? This is like 2007. And they would get polarized reviews where some reviewers would say, this would change everything. It'd be so useful to be more transparent with our work. And others saying, but researchers don't like sharing their data. Why would we do that?
And why would researchers not want to share their data? Yeah, it's based on the academic reward system. Publication is the currency of advancement. I need publications to have a career, to advance my career, to get promoted. And so the work that I do that leads to publication
I have a very strong sense of, oh, my gosh, if others now have control of this, my ideas, my data, my designs, my solutions, then I will disadvantage my career.
Yeah. And here's the irony is that almost every academic would say, of course, science is supposed to be transparent. Of course, we're doing research for the public good. Of course, this is all to be shared. But come on, Pollyanna, we live in a world, right? The reality here is that there is a reward system and I have to have a career in order to do that research.
And so, yes, we can talk all about those ideals of transparency and sharing and rigor, reproducibility. But if they're not part of the reward system, you're asking me to either behave by my ideals and not have a career or have a career and sacrifice some of those ideals.
And in the end of that, 2015, we published The Findings, which was a 270 co-author paper of 100 replications of findings from three different journals in psychology. We got a little less than half of the findings successfully replicated.
Little less than half of the findings successfully replicated.
A year and a half ago, we published the results of the reproducibility project in cancer biology doing the same kind of process and found very similar results. Less than half of the findings in preclinical cancer research replicated successfully when we tried to do so.
That doesn't mean that the original finding is necessarily wrong. We could have screwed something up in the replication. Successfully replicating doesn't mean the interpretation is right. It could be that both the findings have a confound in them, but we just are able to repeat the confound.
Fraud is the ultimate corrosive element of the system of science because as much as transparency provides some replacement for trust – You can't be transparent about everything. So the ideal model in scholarship is that you can see how it is they generated their evidence, how they interpreted their evidence, what the evidence actually is, and then independent people can interrogate that.
And so to the extent that fraud intrudes and actually the evidence isn't It isn't actual evidence. Then the whole edifice of that scholarly debate and tangling with ideas falls apart because you're actually tangling with ideas that aren't based on anything. How familiar are you with the Joachim Bolt situation? I'm not recalling that name, but I may know the case if you describe it.
We can't say with any confidence where it's most prominent. We can only say that the incentives for doing it are everywhere. And some of them gain more attention because, for example, Francesca's findings are interesting. They're interesting to everyone. So of course, they're going to get some attention to that. Whereas the anesthesiologist's findings are not interesting. They put people to sleep.
Until they kill you. Well, yeah, I guess they put you, and then they kill you.
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