Type-II error in Hypothesis Testing I am new to Statistics and was reading about Decision errors in Hypothesis Testing. My question is that why is Type-II error an error at all? From what I understand, it arises when we fail to reject a false null hypothesis. When we fail to reject null hypothesis, it simply means that we do not have strong evidence to reject it. We are not making any comment about which of the two hypothesis is true (or false) . Either can be true. We are not saying that the null hypothesis is true. Then, why is such a conclusion called an error?
Basically, it's called an error because you're making the wrong decision. Like for instance, let's say your null hypothesis is something like: The patient does not have cancer. And the Alternative: The patient does have cancer. A type 2 error in this regard would be like saying "Oh, this patient does not have cancer" while they actually do have cancer. We are FAILING to REJECT a FALSE null. So I guess the most simple way to explain this is, we aren't catching a mistake, and letting it slide by. Of course this isn't all there is to it, but it should help kind of show what a type 2 error is and why it's something you want to avoid.