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Comment by Jonathan

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Rootclaim blog author
We provide three methods to estimate this number, all of them supporting a conclusion that this conditional probability is over 1%. Given that each of these provides a minimum estimate, and some are independent alternative explanations for an HSM cluster, a reasonable final estimate would be 5-10%. We still conservatively assign it 1%, which is enough to make this evidence negligible.
AI Verified source (Apr 1, 2024)
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Bayesian analysis is the right framework for settling the COVID-19 origins question
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AI Verified ai_verified: In source context, the quote is part of the article’s Bayesian/likelihood-ratio analysis of COVID origins: the author says evidence strength is measured by conditional probabilities and Bayes factors, then uses this quoted estimate of p(HSM|Lab Leak, Wuhan) to argue the Huanan market cluster is only negligible evidence. That makes support for Bayesian analysis as the proper framework substantially more likely. ([blog.rootclaim.com](https://blog.rootclaim.com/covid-origins-debate-response-to-scott-alexander/)) · YouCongress gpt-5.4-2026-03-05 · 2h ago
AI Verified Relevant: in context, the quote is part of the source’s Bayes-factor/conditional-probability analysis of the COVID-origin evidence, specifically estimating p(HSM|Lab Leak, Wuhan) and concluding the HSM cluster is weak evidence. The article also explicitly presents this probabilistic approach as the best way to approach the COVID-origins question, so the quote gives a clear, determinable stance signal on the complete statement. ([blog.rootclaim.com](https://blog.rootclaim.com/covid-origins-debate-response-to-scott-alexander/)) · YouCongress gpt-5.4-2026-03-05 · 2h ago
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AI Verified The quote applies a conditional-probability estimate to argue the HSM evidence is "negligible," and the surrounding article explicitly says COVID-origins evidence should be evaluated by "the ratio of the conditional probabilities" (Bayes factors) and calls this "the best way to approach this question." So, even though the quote itself does not say "Bayesian," the source context makes support for that framework clear. ([blog.rootclaim.com](https://blog.rootclaim.com/covid-origins-debate-response-to-scott-alexander/)) · YouCongress gpt-5.4-2026-03-05 · 2h ago
AI Verified For. The quote itself uses Bayesian-style reasoning about a “conditional probability” and says that estimate is “enough to make this evidence negligible.” In the source context, the author explicitly says the strength of evidence is measured by “the ratio of the conditional probabilities” (the Bayes factor) and calls this “the best way to approach this question,” so the article clearly endorses Bayesian/probabilistic analysis as the proper framework for resolving COVID-origin evidence. ([blog.rootclaim.com](https://blog.rootclaim.com/covid-origins-debate-response-to-scott-alexander/)) · YouCongress gpt-5.4-2026-03-05 · 2h ago

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AI Verified The quote appears verbatim in the Rootclaim article at the supplied URL (line 81), and that page’s byline/date are “Jonathan” and “April 1, 2024.” Rootclaim’s author archive also lists the same post under “Jonathan,” corroborating the attribution. The stored wording, author, date, and source URL match the fetchable source. ([blog.rootclaim.com](https://blog.rootclaim.com/covid-origins-debate-response-to-scott-alexander/)) · YouCongress gpt-5.4-2026-03-05 · 2h ago
AI Verified The quote appears verbatim on the cited Rootclaim Blog page, "COVID origins debate: Response to Scott Alexander," and the page byline attributes that post to Jonathan on April 1, 2024. The Rootclaim author archive also lists the same article under Jonathan, corroborating the attribution. No correction is needed. ([blog.rootclaim.com](https://blog.rootclaim.com/covid-origins-debate-response-to-scott-alexander/)) · YouCongress gpt-5.4-2026-03-05 · 2h ago
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