Leif Nelson

And that's before we get to anything like fraud, like the active fabrication of data or manipulation of data.

Yeah. Well, Stephen, you were asking a question that is pretty heavy and one that I'm not particularly well-equipped to answer. If you'd asked me five years ago, I think I would have been more refined in my answer and I would have said, no, that's not a slippery slope problem. There's a slippery slope between I collect five measures and report one versus I collect 10 measures and I report one.

Yeah. Well, Stephen, you were asking a question that is pretty heavy and one that I'm not particularly well-equipped to answer. If you'd asked me five years ago, I think I would have been more refined in my answer and I would have said, no, that's not a slippery slope problem. There's a slippery slope between I collect five measures and report one versus I collect 10 measures and I report one.

Yeah. Well, Stephen, you were asking a question that is pretty heavy and one that I'm not particularly well-equipped to answer. If you'd asked me five years ago, I think I would have been more refined in my answer and I would have said, no, that's not a slippery slope problem. There's a slippery slope between I collect five measures and report one versus I collect 10 measures and I report one.

That's slippery slope. But making up data feels qualitatively different. And I still largely stand by that view. But there have been enough anecdotes that other people, whistleblower types, have presented to us that sound a lot more like someone says, yeah, you know, at first you do the thing where you drop some measures or drop a condition or you remove the outliers.

That's slippery slope. But making up data feels qualitatively different. And I still largely stand by that view. But there have been enough anecdotes that other people, whistleblower types, have presented to us that sound a lot more like someone says, yeah, you know, at first you do the thing where you drop some measures or drop a condition or you remove the outliers.

That's slippery slope. But making up data feels qualitatively different. And I still largely stand by that view. But there have been enough anecdotes that other people, whistleblower types, have presented to us that sound a lot more like someone says, yeah, you know, at first you do the thing where you drop some measures or drop a condition or you remove the outliers.

And then also you take participant 35 and you change their answer from a 7 to a 9. You're like, whoa, that last one doesn't sound the same. But maybe there's some psychology for that, that it feels like it's an extension.

And then also you take participant 35 and you change their answer from a 7 to a 9. You're like, whoa, that last one doesn't sound the same. But maybe there's some psychology for that, that it feels like it's an extension.

And then also you take participant 35 and you change their answer from a 7 to a 9. You're like, whoa, that last one doesn't sound the same. But maybe there's some psychology for that, that it feels like it's an extension.

The very first blog post we posted was about identification of fraudulent data in a paper published 10 years ago. And that one was discovered because Yuri had made a chart for a totally different paper where he was mining data from multiple published studies to just make a chart. And I looked at his figure of this other research group's data and said, that seems unusual.

The very first blog post we posted was about identification of fraudulent data in a paper published 10 years ago. And that one was discovered because Yuri had made a chart for a totally different paper where he was mining data from multiple published studies to just make a chart. And I looked at his figure of this other research group's data and said, that seems unusual.

The very first blog post we posted was about identification of fraudulent data in a paper published 10 years ago. And that one was discovered because Yuri had made a chart for a totally different paper where he was mining data from multiple published studies to just make a chart. And I looked at his figure of this other research group's data and said, that seems unusual.

I want to go read that paper. And so I read the paper and then looked at that data set. In that one, it had collected data on a nine-point interval scale, so people can answer one, two, three, up through nine. And there were numbers in the data set that were things like negative 1.7. And so you say, oh, okay, we're done. Nothing fancy.

I want to go read that paper. And so I read the paper and then looked at that data set. In that one, it had collected data on a nine-point interval scale, so people can answer one, two, three, up through nine. And there were numbers in the data set that were things like negative 1.7. And so you say, oh, okay, we're done. Nothing fancy.

I want to go read that paper. And so I read the paper and then looked at that data set. In that one, it had collected data on a nine-point interval scale, so people can answer one, two, three, up through nine. And there were numbers in the data set that were things like negative 1.7. And so you say, oh, okay, we're done. Nothing fancy.

Once you open the data set, you can then close it and say it's broken.

Once you open the data set, you can then close it and say it's broken.

Once you open the data set, you can then close it and say it's broken.

Professor Gino has indicated that she has done nothing wrong. And we have said that the data in those four papers contain evidence that strongly suggests that there is fraud.