csaid81

Yes, but not only did they improve the memory of mouse models of Alzheimer's, they also improved the memory of older wild-type mice, which seems impressive to me. https://www.nature.com/articles/s41586-025-09335-x/figures/1...

I know a lot of legitimate research supports various versions of the amyloid hypothesis, but I don't buy that these likely fraudsters had minimal impact.

You said that Lesné was "not that highly cited". But his main fraudulent paper was cited 2,300 times, making it the fifth most highly cited Alzheimer's paper since 2006! [1]

Berislav Zlokovic's likely fraudulent papers were cited 11,500 times! [2]

It's hard to imagine these highly papers didn't redirect at least some scientists to do pointless followup studies. Of course, in the counterfactual world the scientists might still have been doing pointless studies, but we'll never know...

[1] https://www.science.org/content/article/potential-fabricatio... [2] https://www.science.org/content/article/misconduct-concerns-...

Sorry, here's the correct link https://chris-said.io/2024/06/17/the-case-for-criminalizing-...

He could be prosecuted under current fraud laws, but this hardly ever happens.

I wrote a blog post on how to make this easier, including a new criminal statute specifically tailored for scientific fraud. https://news.ycombinator.com/item?id=41672599

Blog post author here. The paper was the 4th most cited paper in Alzheimer's research since 2006. So I feel reasonably confident that if it had never been written, some researchers at the margin would have chosen to work on other hypotheses instead, and perhaps those other avenues would have been more fruitful.

How much time could have been saved towards an effective treatment? It could be as high as a decade, but of course more likely it was zero years. I averaged it out to 1 year.

Now suppose you think that 1 year is orders of magnitude too high, and that in expectation it averages out to a 1 day delay. Even then, I estimate 100,000 QALYs would be lost, making this a tragically high impact case of misconduct.

--- Final point: Nobody doubts that science is error correcting. The point is that the errors are corrected far too slowly and many never get corrected at all. It's incredibly hard to develop good theories when you know that 30-50% of the results in your lit review are false.

I think the answer is not "nope" but "probably nope". That video is perhaps overconfident in our ability to detect these storms.

From the article:

> One-third of major storms arrive unexpectedly, according to the SWPC’s own 2010 analysis. And that’s not just the small storms. According to a news article in Science, the SWPC might be also be poor at identifying the characteristics of severe storms, since they are so rare.

Thanks for the link! Good stuff.

By the way, dplyr doesn't use multi-indexes. I actually think this one of the reasons (although not the biggest reason) dplyr is easier to use.

Pandas makes lots of backwards-incompatible changes. See for example these changes in the latest release

http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew...

There have been a few independent attempts to add dplyr-like functionality to pandas without being backwards incompatible (e.g. dplython). I'd be very happy if the core pandas team went down this path.

That being said, I don't have a good understanding of how strong the distinction is between "design issues" and "issues where money helps". There must be some overlap.

Here's some specific examples:

https://twitter.com/Chris_Said/status/715249097326768128

https://twitter.com/Chris_Said/status/861244045535756290

I could be wrong, but I'm pretty sure that these would be solved by pandas API design improvements, not with numpy improvements under the hood. (NB: As always, a big thanks to the developers for all their work.)

Right, but I'm talking about the pandas API. Stuff like how easy it is to remember exactly how to do aggregations, transformations, etc.

It's great that the Moore Foundation provided funding for open source data science tools in Python. Good for them!

That being said, I do wonder if numpy is the most appropriate recipient. In my experience with data science, the tool that would benefit the most is not numpy, but pandas. While data scientists rarely use numpy directly, every data scientist I know who uses pandas says they are constantly having to google how to do things due to a somewhat confusing and inconsistent API. I use pandas at work every day and I'm always looking stuff up, particularly when it comes to confusing multi-indexes. In contrast, I rarely use R's dplyr at work, but the API is so natural that I hardly ever need to look things up. I would love if pandas could make a full-throated commitment to a more dplyr-like API.

Nothing against pandas -- I know the devs are selflessly working very hard hard. It's just that it seems there is more bang for the buck there.

Yup, I agree about throwing away rater information. The actual application at my company that motivated me to research this doesn't have rater information, which is why I didn't think to adjust for it. The movie case was just an example I used to motivate this post for which, yes, I agree, rater information would be quite useful.

Got it. Thanks for the clarification. In that case I would think that James-Stein / Buhlmann / BLUP is a better approach, since it is just as easy to implement and the amount of shrinkage is optimally chosen based on the data, rather than on guesswork. In fact it may be more easy because no guesswork is required.

It would be interesting though to have people try to guess suitable values of m and C and then see how close their MSEs get to the James-Stein MSE. I suspect that some people's guesses would be meaningfully off target.

This seems different and a bit lacking in detail (although I don't dispute that it could be useful). How exactly does one choose m and C? And what are the conditions under which it would reduce to the James-Stein / Bulmannn / BLUP model?

Author here. Please see the section on mixed models in my post. As I mentioned there, I would love if an expert could expand on the relationship between mixed effects and Empirical Bayes.

Regarding MCMC, one of the things I try to emphasize throughout the post is that the best solution depends on your needs (for example if you want a full posterior). In fact, most of the post is devoted to quick and simple methods -- not MCMC -- because they are good enough for most purposes. I welcome your feedback though on how I could make this point clearer.

It's an excellent blog post, although it's worth emphasizing that it is designed for the binomial case, where you wish to compute the fraction of occurrences within some events, such as batting averages. For continuous variables, however, it makes more sense to use one of the methods described in the original post.

TL;DR: One blog post is for Rotten Tomatoes and the other is for Metacritic.