> formed in frequentist terms I suspect that this is simply a sense of scale, with the freq-vs-bayes aspect playing only a minor role. The normalization that

> formed in frequentist terms I suspect that this is simply a sense of scale, with the freq-vs-bayes aspect playing only a minor role. The normalization that happens when you use percentages and 0.0 -> 1.0 probabilities is often useful, but it can sometimes obscure the magnitude of some relationships. It's easier to understand the sense of scale when you say "1 out of every 100,000 people" (which is easily extended to "a handful of people in a large city"). The same information is presented as "0.001% of the population" requires the reader to do more mental math if they want to understand how many people could be affected. Knowing how to interpret false-positives and false-negatives is very important, but doctors are busy people who have to memorize a lot of data. It is probably better for patients if they can at least remember if something is "common" vs "rare" when they need to make a quick decision.