zlacker

>>delbar+(OP)
> When you see a reliable public betting challenge with real stakes, you can be very confident the claim is true at a probability that is significantly above 50% (assuming 1:1 odds are being offered).

This makes the assumption that the overall stakes are even. What if Alex Jones puts up 100K that boiled frog eyes cure COVID? He could be absurdly wrong, but the additional credibility offered by this may let him sell a million dollars more of boiled frog eyes.

The problem is, there is going to be virtually no research on whether boiled frog eyes are a cure for covid, so it is next to impossible to challenge the claim and made all the more difficult by the potential for human error and the risk involved noted in the challenge.

>>MattGa+D4
The assumption the writer makes about correlation of odds and probability is nonsensical - betting houses change the odds regularly so they make money based on essentially random bettors current intuition.

>>steven+a6
Exactly, like under the 'WIV lab procedures' section it states:

Probabilistic Estimates:

Labs with lax security and procedures are conservatively estimated as 2x more likely to produce a lab escape.

However, since the reports are not very reliable, this is reduced to 1.5x.

There's no explanation or methodology presented as to how they derive these probabilities based on articles they found in the news and most of their assertions rely on this inexplicable methodology. Another example of just how nonsensical their conclusions are, under the sections 'WIV disassociation' and 'Chinese response' they again rely on news articles to conclude China's response merits suspicion and therefor must be covering up the source of COVID. This assertion is just illogical and fatally flawed. While China may have motivation to cover it up if that were the case, scientists in other countries who have obviously spent a great deal of time examining COVID and concluded it's zoonotic wouldn't have motivation to cover it up. Yet this obvious logical deduction is not factored into their conclusions or even merits a mention.

>>weare1+Cf
The nice thing about Bayesian analysis is that you have to make your priors explicit. That’s what this is, a prior. It doesn’t have to have a derivation; it can just be a guess. The important thing is that you’re writing your guess down so everyone can see it.

You can then also do a sensitivity analysis to figure out how much your conclusions change if you modify your priors. So if you, the reader, think the priors are wrong, then you can change them and re-do the analysis.

I think the most interesting thing about this analysis is exactly that: we can look at the priors and come up with a principled conclusion. We can then argue about whether the priors are right.