I'm confused about two seemingly different approaches to errors. I'm looking at a non-stat book that uses the false pos/neg approach to discuss type 1 and 2 errors. The author is discussing cognitive errors (You misjudge a rustle of grass in the field as not a snake, but it is a snake. It seems this (false pos/neg) has more to do with the Bayesian approach than the usual testing of a null hypothesis.
Another book, The Cartoon Guide to Statistics (actually a good read to introducing people to statistics) talks to both ideas. The "false pos/neg approach is decidedly Bayesian. He really only addresses the false positive in an example. He does not mention error. Much later he gets into hypothesis testing with mention of type 1 and 2 errors.
Years ago I worked with medical people and they liked to use the false pos/neg concept. It seems that's the typical approach in medical articles I've read. Maybe the idea of "false" appeals to them more than the idea of type. Quite possible it's used in psychology studies.
I would gather that we do have two ways of playing with errors. One is based on the idea of conditional probabilities and the other on probability distributions. Comments?