You say that like detail is a pure good. “Greg Cochran says 1.2%” is better than any number of words from CBG. Anyhow, you repudiated this. When I pushed you on it, you came up with the number 1.4%.
I’m not confident in a 1% as an upper limit (especially in an overrun healthcare system) but I do think that comment gives good back-of-the-envelope estimates (as requested). Later on in that thread CBG also acknowledges it may be higher in than 1% in some places and conditions.
Detail in this case is useful as it shows multiple sources and back-of-the-envelope calculations. I’m not really assessing CBG (except trusting that he isn’t picking and choosing his arguments), rather I’m assessing his back-of-the-envelope calculation and where likely errors can creep in—exactly what the great-grandparent mentioned was preferred.
If “Greg Cochran says 1.2%” is the counter-argument then I don’t really know what to say except how likely is it that he’s wrong this time and by what factor might he be off? What’s his confidence interval? If someone can provide his working then at least that’s something I can assess. It seems he is looking specifically at places with high infection rates and more stretched healthcare systems.
Anyhow, you repudiated this. When I pushed you on it, you came up with the number 1.4%.
The naive central estimate of a single back-of-the-envelope estimate where virus prevalence in Lombardy was estimated from one small town from a month previous isn’t something I’d put much weight on. If pushed for an interquartile range based only on this calculation I would say 0.5<IFR<3.5. The point of that calculation wasn’t to get an accurate answer but to show that 0.2% population fatality rate doesn’t imply that the IFR is massive and 3,000,000 US coronavirus deaths this year is still highly unlikely.
What’s CBG’s confidence interval? When he says 0.5-1%, does he mean something? Does he mean a confidence interval, or a distribution of “normal” situations or a distribution of more general situations? Or does he not mean anything?
Later on in that thread CBG also acknowledges it may be higher in than 1% in some places and conditions.
It’s nice that he says that, but that’s exactly the situation that you cited him in the other thread, claiming <=1%. I’m guessing that the pseudo-detail is exactly what caused you to not understand his claims. If you don’t know what he claims, how can you assess his work? At least with GC you’re not fooling yourself about what you’ve done.
And I still don’t know what he claims. He seems to claim that NYC had IFR <=1%. Was NYC normal or not? In any event he’s wrong. If NYC defines the upper range, then this affects his conclusion. If NYC doesn’t count, I dunno, but I’m pretty sure that people are equivocating on whether it counts.
You say that like detail is a pure good. “Greg Cochran says 1.2%” is better than any number of words from CBG. Anyhow, you repudiated this. When I pushed you on it, you came up with the number 1.4%.
I’m not confident in a 1% as an upper limit (especially in an overrun healthcare system) but I do think that comment gives good back-of-the-envelope estimates (as requested). Later on in that thread CBG also acknowledges it may be higher in than 1% in some places and conditions.
Detail in this case is useful as it shows multiple sources and back-of-the-envelope calculations. I’m not really assessing CBG (except trusting that he isn’t picking and choosing his arguments), rather I’m assessing his back-of-the-envelope calculation and where likely errors can creep in—exactly what the great-grandparent mentioned was preferred.
If “Greg Cochran says 1.2%” is the counter-argument then I don’t really know what to say except how likely is it that he’s wrong this time and by what factor might he be off? What’s his confidence interval? If someone can provide his working then at least that’s something I can assess. It seems he is looking specifically at places with high infection rates and more stretched healthcare systems.
The naive central estimate of a single back-of-the-envelope estimate where virus prevalence in Lombardy was estimated from one small town from a month previous isn’t something I’d put much weight on. If pushed for an interquartile range based only on this calculation I would say 0.5<IFR<3.5. The point of that calculation wasn’t to get an accurate answer but to show that 0.2% population fatality rate doesn’t imply that the IFR is massive and 3,000,000 US coronavirus deaths this year is still highly unlikely.
Well, don’t do that. I told you this before.
What’s CBG’s confidence interval? When he says 0.5-1%, does he mean something? Does he mean a confidence interval, or a distribution of “normal” situations or a distribution of more general situations? Or does he not mean anything?
It’s nice that he says that, but that’s exactly the situation that you cited him in the other thread, claiming <=1%. I’m guessing that the pseudo-detail is exactly what caused you to not understand his claims. If you don’t know what he claims, how can you assess his work? At least with GC you’re not fooling yourself about what you’ve done.
And I still don’t know what he claims. He seems to claim that NYC had IFR <=1%. Was NYC normal or not? In any event he’s wrong. If NYC defines the upper range, then this affects his conclusion. If NYC doesn’t count, I dunno, but I’m pretty sure that people are equivocating on whether it counts.
I have edited the original comment to more fully reflect my position.