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Comment by cozzyd | Hacker News Reader

It's not all of Stanford, just one group (Jay Bhattarcharya's) that published a paper that betrayed extreme ignorance of binomial confidence intervals and modeling in general and is now the subject of this article. Why anybody would listen to this group despite after that is a good question (although, since poor understanding of statistics is rampant in the Medical field, let alone among the general population, perhaps not too surprising).

(disclaimer: Stanford '09)

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It's Battarcharya and a few others, including Ioannidis. However Stanford is completely complicit, and when I search for these people I get results like this that promote bad information and reasoning:

https://fsi.stanford.edu/news/coronavirus-deadly-they-say

Rather than anything from the university that promotes the more accurate statements from the rest of the faculty.

So I would say that Stanford carries a lot of blame for letting their name be used for disinformation, and is not pushing back at all, which is a huge disgrace. If you are an alum, make some noise and refuse to make contributions until they start at least giving equal PR time to the scientifically based viewpoint.

Edit: and how could I forget Michael Levitt, who is now following in Kary Mullis' footsteps as a Nobel winning biochemist that promotes for crazy viral theories that damage public health

https://www.stanforddaily.com/2020/05/04/qa-nobel-laureate-s...

Well the Daily is the independent student newspaper and has little to do with official University policy. I don't know what FSI is, perhaps some department that Bhattarcharya is part of. I agree, if it was being pushed by official Stanford news sources (a search for Bhattarcharya on https://news.stanford.edu/ reveals nothing, although plenty of other COVID-related research), that that might be problematic. I agree it's not a good look for Stanford, but but if people conflate researchers from a university with a university, then that's on them.

That “conflation” is the exact same thing as the University’s reputation. If they don’t value their reputation, fine, but these bad researchers are only getting press because they are attaching the univerity’s name to their ideas. That’s not something to blame readers for. And if there are researchers at these universities that oppose them, it’s up to the university and other researchers at Stanford to stop their name being use if they disagree with its use.

Well I might be too knee-deep in academia to be a fair judge, but every university is a bunch of independent researchers of variable quality with wide latitude to pursue whatever and it seems silly to judge a university's reputation based on some individual researcher (or alumni, or whatever). I think any conflation is more the fault of media articles about the research than anything else. Yes, you write your affiliation on papers, but that does not really construe any sort of endorsement. I'm sure one can find many Stanford faculty expressing contrary viewpoints, but those are not (for whatever reason) being amplified by the media.

I can put a crazy paper on the arXiV tomorrow with my affiliation and if some media report says "The University of Chicago is claiming a perpetual motion machine" or whatever, that's the media's fault for misrepresenting the situation.

Curious, explain to me why I should listen to a random person on the Internet over a university research group? "betrayed extreme ignorance of binomial confidence intervals and modeling", lots of words but zero evidence supporting your assertions. If you have any concrete evidence to contradict their findings rather than just a blanket "extreme ignorance of binomial confidence intervals", that would be far more useful.

Don't trust one research group, trust the rest of the scientific body rising up to demolish them for obviously bad work:

https://arstechnica.com/science/2020/04/experts-demolish-stu...

I think calling it "extreme ignorance of binomial confidence intervals" is incorrect and far too charitable. The authors were intentionally deceitful. Co-authors of the preprint requested that they be taken off the pre-print because they had warned that the statistics were bad. Prior the the prevalence study, they had cherry picked weird data sets and published an editorial. They are clearly trying to bend the data to their preconceived notions, in a really public way.

To see journalists pick up their work again, just because they are at a big name institution, is a huge betrayal of the public trust.

> scientific body rising up to demolish them for obviously bad work

Come on man, let's be fair: a couple of tweets and a few snarky emails hardly consist of the "scientific body demolishing them." I'm not sure if I agree with Bhattacharya's study, but it's hardly been universally repudiated. This just low-effort politicizing by Ars Technica (which has a spotty record to begin with).

Even this critical blog post[1] has all kinds of colorful discussion (mostly by academics) showing that things are simply not as cut-and-dry as we'd like them to be.

[1] https://statmodeling.stat.columbia.edu/2020/04/19/fatal-flaw...

Worth looking through here and related links: https://statmodeling.stat.columbia.edu/2020/04/19/fatal-flaw...

No reason you should listen to me in particular (no doubt I have made egregious statistical errors myself in my research), but a good place to start might be https://en.wikipedia.org/wiki/Binomial_proportion_confidence... or, as others have posted, it's worth listening to Andrew Gelman (unlike me, a statistics expert) about this! https://statmodeling.stat.columbia.edu/2020/04/19/fatal-flaw...

Essentially the problem with the study is that the bounds on the false positive rate of the test used was such that the data are completely consistent with all of the positives being false positives.

Thanks!

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