You notice a minority of scientists were early to advocate: 1. asymptomatic COVID19 occurs 2. masks are useful 3. rapid tests are useful Yet opposition on each came from other scientists. Why? Is it because training has promoted p<0.05 to the point we can't do Baynesian anymore?
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Replying to @michaelzlin
each of these has big implications for the public, and being wrong can be very damaging. by the same 'Bayesian' reasoning we'd skip clinical trials for the vaccine. is it crazy to expect scientists to provide evidence to support their claims?
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Replying to @JaseGehring @michaelzlin
not to mention the extremely obvious counterpoints, including: - hydroxychloroquine - Remdesivir - convalescent plasma - excessive surface sterilization you can't cherry pick correct ideas and say we need to start adopting ideas more quickly. some ideas are right, others not.
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Replying to @JaseGehring
Not cherry picking. The scientists I am thinking about did not advocate cq/hcq or excessive surface cleaning, indeed because those lacked mechanistic basis. That's the whole point, to use all knowledge intelligently. You can go back through my posts or
@zeynep 's3 replies 0 retweets 1 like -
Replying to @michaelzlin @JaseGehring
You were never going to have RCTs for aerosols. There are things that do require clinical trials, and there are things that can work with other kinds of evidence in science. What’s the risk? What’s the harm? What’s the previous science and our understanding of causal mechanisms?
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The key to success was clearly being able to distinguish the two. I’m not even sure I’d call it Bayesian. Just matching the right epistemology to the question. MDs think in clinical trials for drugs—appropriately—but that’s not how you figure out aerosols or all public health.
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