Why we don’t have more studies on Taffix or increasing humidity in schools is not a matter of much attention seem like Inadequate equilibria type of problem, which seem quite distinct from evaluating existing evidence on some topic.
Failure to see an effect doesn’t mean that effects are disproven but it does mean that we don’t know whether the effect exists.
Sorry for too much brevity before.
No. Per Bayes theorem, failure to see an effect in an analysis/experiment where you would expect to see no effect no matter if the effect exists or not should make you to stay with the prior.
In the specific case of this topic and post—someone looking at masks clearly will likely have a prior “they work, but they aren’t a miracle cure”. More precisely, this could be expressed roughly as an expected effect distribution in R reduction space with almost all of the mass centered somewhere between 5% a 50%. Different reasonable observers looking at different data will likely arrive at somewhat different maximum likelihood estimates and shapes of the distribution, but they will have very little probability mass on no effect, or harm, and very little on large effect.
Should someone with a prior from this class update the prior, based on the evidence consisting of the analysis by Mike Harris?
Not at all! Posterior should stay the same.
Should someone with this prior update the prior, based on reading the referenced paper?
In my view yes, I think bayesians should update away even more from very low effects (like 5%) or very high effects (like 50%).
Why we don’t have more studies on Taffix or increasing humidity in schools is not a matter of much attention seem like Inadequate equilibria type of problem, which seem quite distinct from evaluating existing evidence on some topic.
The point is that the evidence we have for Taffix and the evidence for humidity is better then the evidence we have for masks.
No. Per Bayes theorem, failure to see an effect in an analysis/experiment where you would expect to see no effect no matter if the effect exists or not should make you to stay with the prior.
The prior before we run studies is that we don’t know whether or not masks work. Studies are the only way to move from “We think it’s likely that masks work” to “We know that masks work”.
Why we don’t have more studies on Taffix or increasing humidity in schools is not a matter of much attention seem like Inadequate equilibria type of problem, which seem quite distinct from evaluating existing evidence on some topic.
Sorry for too much brevity before.
No. Per Bayes theorem, failure to see an effect in an analysis/experiment where you would expect to see no effect no matter if the effect exists or not should make you to stay with the prior.
In the specific case of this topic and post—someone looking at masks clearly will likely have a prior “they work, but they aren’t a miracle cure”. More precisely, this could be expressed roughly as an expected effect distribution in R reduction space with almost all of the mass centered somewhere between 5% a 50%. Different reasonable observers looking at different data will likely arrive at somewhat different maximum likelihood estimates and shapes of the distribution, but they will have very little probability mass on no effect, or harm, and very little on large effect.
Should someone with a prior from this class update the prior, based on the evidence consisting of the analysis by Mike Harris?
Not at all! Posterior should stay the same.
Should someone with this prior update the prior, based on reading the referenced paper?
In my view yes, I think bayesians should update away even more from very low effects (like 5%) or very high effects (like 50%).
The point is that the evidence we have for Taffix and the evidence for humidity is better then the evidence we have for masks.
The prior before we run studies is that we don’t know whether or not masks work. Studies are the only way to move from “We think it’s likely that masks work” to “We know that masks work”.