csaid81

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.

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.