I am not sure I know what the most impactful thing to do is, by edu level. Let me think about it.
My intuition is the best thing for “raising the sanity waterline” is what the LW community would do with any other bias: just preaching association/causation to the masses that would otherwise read bad scientific reporting and conclude garbage about e.g. nutrition. Scientists will generally not outright lie, but are incentivized to overstate a bit, and reporters are incentivized to overstate a bit more. In general, we trust scientific output too much, so much of it is contingent on modeling assumptions, etc.
Explaining good clear examples of gotchas in observational data is good: e.g. doctors give sicker people a pill, so it might look like the pill is making people sick. It’s like the causality version of the “rare cancer ⇒ likely you have a false positive by Bayes theorem”. Unlike Bayes theorem, this is the kind of thing people immediately grasp if you point it out, because we have good causal processing natively, unlike our native probability processing which is terrible. Association/causation is just another type of bias to be aware of, it just happens to come up a lot when we read scientific literature.
If you are looking for some specific stuff to do as a programmer, email me :). There is plenty to do.
I am not sure I know what the most impactful thing to do is, by edu level. Let me think about it.
My intuition is the best thing for “raising the sanity waterline” is what the LW community would do with any other bias: just preaching association/causation to the masses that would otherwise read bad scientific reporting and conclude garbage about e.g. nutrition. Scientists will generally not outright lie, but are incentivized to overstate a bit, and reporters are incentivized to overstate a bit more. In general, we trust scientific output too much, so much of it is contingent on modeling assumptions, etc.
Explaining good clear examples of gotchas in observational data is good: e.g. doctors give sicker people a pill, so it might look like the pill is making people sick. It’s like the causality version of the “rare cancer ⇒ likely you have a false positive by Bayes theorem”. Unlike Bayes theorem, this is the kind of thing people immediately grasp if you point it out, because we have good causal processing natively, unlike our native probability processing which is terrible. Association/causation is just another type of bias to be aware of, it just happens to come up a lot when we read scientific literature.
If you are looking for some specific stuff to do as a programmer, email me :). There is plenty to do.