Maybe this is obvious to you, but a lot of the content on this site is about explicating common errors of logic and statistics that people might fall for. I think it’s valuable.
Thank you. Maybe I over-indexed on using the satellite example, but I thought it made for a better didactic example in part because it was so obvious. I provided the other examples to point to cases where I thought the error was less clear.
The lesson to draw from the false confidence theorem is “be careful,” not “abandon all the laws of ordinary statistics in favor of an alternative conception of uncertainty.”
This is also true. Like I said (maybe not very clearly), there's more or less 2 solutions--use non-epistemtic belief to represent uncertainty, or avoid using epistemic uncertainty in probability calculations. (And you might even be able to sort of squeeze the former solution into Bayesian representation by always including "something I haven't thought of" to include some of your probability mass, which I think is something Eliezer has even suggested. I haven't thought about this part in detail.)
I didn't look for, and so was not aware of, any larger community. I found the 2 linked papers and, once I realized what was going on, recognized the apparent error in a few places. I agree that "decreasing the quality of your data should not make you more confident" is obvious when stated that way, but like with many "obvious" insights, the problem often comes in recognizing it when it comes up. I attempted to point this out to Micheal Weissman in one of the ACX threads (he did a Bayesian analysis of lab leak, similar to Rootclaim's) and he repeatedly defended arguments of this form even after I pointed out that he was getting reasonably large Bayes Factors based entirely on epistemic uncertainty.
Did you read section 2c of the paper? It seems to be saying something very similar to the point you made about the tracking uncertainty:
for a fixed S/R [relative uncertainty of the closest approach distance] ratio, there is a maximum computable epistemic probability of collision. Whether or not the two satellites are on a collision course, no matter what the data indicate, the analyst will have a minimum confidence that the two satellites will not collide. That minimum confidence is determined purely by the data quality... For example, if the uncertainty in the distance between two satellites at closest approach is ten times the combined size of the two satellites, the analyst will always compute at least a 99.5% confidence that the satellites are safe, even if, in reality, they are not...
So when you say
then you must content yourself with avoiding approaches within around 100 metres, and you will be on the equivalent of the yellow line in that figure.
Is this not essentially what the confidence region approach is doing?
Or explain why the NYT does use the chosen name of other people, like musicians' stage names.
Brand new account, reposting old arguments? Not suspicious at all.
Stoyan and Chiu (2024)
"Just because the market was the epicenter doesn't mean the pandemic started there," while technically true, is fairly meaningless. If the center were at the lab every lab leak proponent would be shouting at the top of their lungs this conclusively proves the lab leak theory. Debating one particular statistical analysis doesn't disprove the very elementary technique of "look at the data, it's obvious" aka https://xkcd.com/2400/.
The multiple spillover theory might be wrong. But then again, so might all of the analyses that Roko cited in his initial post, including the paper about genetic engineering, the Richard Ebright tweet, the RTK estimates, etc. The point of that part was to show that it's very easy to generate high Bayes factors if you highball favorable pieces of information, ignore unfavorable ones, make convenient assumptions, and multiply numbers together.
https://michaelweissman.substack.com/p/an-inconvenient-probability
This analysis is obviously heavily biased. No Bayes factor at all for the cases being at the market? Again, no LL supporter would seriously say the BF would be one if the cases were clustered near the WIV. This is the exact same sort of highly motivated reasoning that Rootclaim applied, and neither of the judges bought it, for the same reason. The CGG analysis is just wrong, etc.
They're not equally unlikely. You haven't provided any actual evidence for this claim.
Also, why on Earth would we just take the ratio of distances or areas as the probability factor? That's not how pandemics work.
ICUs were overwhelmed because Covid spread so much. Its hospitalization rate is a few percent and its fatality rate is 1% or so. This is in contrast to diseases like SARS 1 (9.5% fatality rate) or MERS (34% fatality rate). Sure, it's not mild compared to seasonal flu, but it is much more mild than the obvious things you would compare it to.
The second thing would be surprising as if the virus can so often jump to humans from animals it will happen closer to its origin in Laos.
Spillover events probably did happen elsewhere, but not all spillover events lead to a pandemic, and covid is usually so mild that it's not surprising we can't find any such cases. (I also don't know if some final important mutation didn't happen until much closer to the actual pandemic start).
Alternative explanation is following: as the market is one of the most crowded place in the city
This is discussed in the Rootclaim debate. There are many different types of places which served as superspreader events early on, the evidence we have shows the growth rate in the market as the same outside of it, and overall growth didn't seem to slow down when they closed the market.
If we assume that a worker of WIH was infected at work, this will be completely unspectacular until he started infecting other people. Such person can commute all around the city including to CDC near wet market.
This is also addressed. It would be a fantastic coincidence--much stronger than the one you posited at the start of this thread--if the only place they brought the disease was one of only a handful of other places in the city that a pandemic could actually start. Like, if all the early cases clustered around the WIV, and I said that a HSM worker could have brought it to the lab, would anyone take that seriously?
This, by the way, is exactly the kind of thing that annoys me and which is one of the main issues I made this thread to address. If you make enough favorable assumptions, you can make any hypothesis look good. This is clearly not the best explanation for the available evidence. Merely because you have successfully epicycled your way into a version of the theory which is not obviously impossible doesn't mean anyone has any reason to think it is even remotely likely. Your arguments aren't even consistent, as you seem surprised that there were no spillovers between Wuhan and Laos, but then don't seem at all skeptical of the idea that a sick person would commute all over the city and only bring it to 1 place.
I mean, I could point out that the first non-Wuhan case was in Beijing on December 17th (I think, going off memory here) and that someone could have gotten sick in a different city, and then just hopped on a train and immediately went to the HSM, and the WIV isn't relevant at all. Is this story convincing? Is there any evidence to support it? Does it feel like I am engaging in truth-seeking, or just throwing shit at a wall and seeing what sticks so I can prop up my pet theory?
What would the disjunctive fallacy be? Failing to account for the fact that P(A or B) >= P(A) and P(B)?
I think this is essentially the solution mentioned in the paper.