Eh, but if you've taken a machine learning course, you should have seen the notion of false positive/false negative when you cover any kind of classification technique.
but they're not actually equivalent, in spite of tables like this [0]. type ii error is a false negative result in the context of a test, where you have to understand which hypothesis is which and exactly what you are accepting or rejecting (hypotheses are not always as simple as hotdog/not-hotdog); if your listener doesn't know what statistical tests mean or wasn't following the setup, they have to stop you and ask.
