4 ms·

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 confiden

by rajup 6y ago

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.

epistasis 6y ago
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-studies-suggesting-covid-19-is-no-worse-than-flu/ 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.
- dvt 6y ago
  > 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-flaws-in-stanford-study-of-coronavirus-prevalence/ https://statmodeling.stat.columbia.edu/2020/04/19/fatal-flaw...
nickforr 6y ago
Worth looking through here and related links: https://statmodeling.stat.columbia.edu/2020/04/19/fatal-flaws-in-stanford-study-of-coronavirus-prevalence/ https://statmodeling.stat.columbia.edu/2020/04/19/fatal-flaw...
cozzyd 6y ago
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_interval 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-flaws-in-stanford-study-of-coronavirus-prevalence/ 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.
- rajup 6y ago
  Thanks!

by rajup 6y ago

epistasis 6y ago

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-studies-suggesting-covid-19-is-no-worse-than-flu/ 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.

dvt 6y ago
> 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-flaws-in-stanford-study-of-coronavirus-prevalence/ https://statmodeling.stat.columbia.edu/2020/04/19/fatal-flaw...

nickforr 6y ago

Worth looking through here and related links: https://statmodeling.stat.columbia.edu/2020/04/19/fatal-flaws-in-stanford-study-of-coronavirus-prevalence/ https://statmodeling.stat.columbia.edu/2020/04/19/fatal-flaw...

cozzyd 6y ago

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_interval 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-flaws-in-stanford-study-of-coronavirus-prevalence/ 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.

rajup 6y ago
Thanks!