From one of Tim Worstall’s “Oh dear” posts:

Maybe inequality and poverty in modern Britain are important and maybe they’re not. It’s entirely possible to argue it either way and to a large extent depends upon your Bayesian priors.

So naturally I had to reply,

Actually this is a value judgment and would be expressed through a utility function. Bayesian priors (and posteriors) are probability distributions expressing subjective degrees of certainty over parameters of interest.

No indication thus far that the blog author has taken this on board…

Is it better that Bayesian concepts are invoked incorrectly rather than not at all? I believe so, but we must continue to strive towards fuller understanding of them amongst non-statisticians. Because otherwise we have to deal with things like this:

First, what did you think was the probability of success in Afghanistan before the mission began? This is the prior probability, which we’ll call Ps. The probability of failure, Pf, is one minus this.

Second, what is the probability that we’d see the number of deaths we have, if the mission were succeeding? Call this Pd|s.

One minus this gives us Pd|f. [emphasis mine, calculation thankfully not]

Again, comments to the contrary had no effect on deflecting the author in his enthusiasm on this occasion. Oh dear.