This calculation just used the fact that Y would have been able to give stronger testimony than X, and that lawyers have incentives to present a strong case for their client where possible. In this scenario, the fact that Y was not called is evidence that Y’s testimony would have weakened the case for Z.
The actual objection against hearsay has nothing to do with this calculation at all, as I mentioned in my comment.
You can apply it in ordinary conversation too (to the extent that you apply Bayesian updates in ordinary conversation at all). It’s just that the update is stronger when the equivalent of E|XYZ is more unlikely, and in ordinary conversation it may not be very unlikely resulting in a weaker update.
This calculation just used the fact that Y would have been able to give stronger testimony than X, and that lawyers have incentives to present a strong case for their client where possible. In this scenario, the fact that Y was not called is evidence that Y’s testimony would have weakened the case for Z.
The actual objection against hearsay has nothing to do with this calculation at all, as I mentioned in my comment.
You can apply it in ordinary conversation too (to the extent that you apply Bayesian updates in ordinary conversation at all). It’s just that the update is stronger when the equivalent of E|XYZ is more unlikely, and in ordinary conversation it may not be very unlikely resulting in a weaker update.