They can be (deterministic Bayesian updating is just causal inference), but they can also not be (probabilistic Bayesian updating requires a large sample size; also, sampling bias is universally detrimental to accurate learning).
Maybe I should have gone into why everyone puts anecdotes at the bottom of the evidence hierarchy. I don’t disagree that they belong there, especially if all else between the study types is equal. And even if the studies are quite different, the hierarchy is a decent rule of thumb. But it becomes a problem when people use it to disregard strong anecdotes and take weak RCTs as truth.
I think so too! A strong anecdote can directly illustrate a cause-and-effect relationship that is consistent with a certain plausible theory of the underlying system. And correct causal understanding is essential for making externally valid predictions.
They can be (deterministic Bayesian updating is just causal inference), but they can also not be (probabilistic Bayesian updating requires a large sample size; also, sampling bias is universally detrimental to accurate learning).
Yep, I agree.
Maybe I should have gone into why everyone puts anecdotes at the bottom of the evidence hierarchy. I don’t disagree that they belong there, especially if all else between the study types is equal. And even if the studies are quite different, the hierarchy is a decent rule of thumb. But it becomes a problem when people use it to disregard strong anecdotes and take weak RCTs as truth.
I think so too! A strong anecdote can directly illustrate a cause-and-effect relationship that is consistent with a certain plausible theory of the underlying system. And correct causal understanding is essential for making externally valid predictions.