Or you can just say "false negative", and every CS major will understand you.

I find people in Math and CS have often very different names for the same type of concepts and they could easy understand each other if they stuck to the more common terms.

In this case, saying: TYPE 2 ERROR, makes you look like you are trying too hard.

It's also extremely confusing because very few people remember type 1 vs 2 but false positive/negative has intuitive understanding.

type ii error is statistics, not mathematics. there is no equivalent concept in CS because type ii error relates specifically to statistical inference and hypothesis testing.

that said, if you are just pointing to a box in a confusion matrix and saying "TYPE II ERROR," you are probably trying too hard.

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.

[0] https://en.wikipedia.org/wiki/Type_I_and_type_II_errors#Tabl...

Granted, Type II error and confusion matrices are covered in more basic statistical classes, and are indeed important for hypothesis testing.

I think the point the parent might have been making is that many people (or maybe just me) know "type II error" by the far more self-explanatory name of "false negative".