I was reasonably mathy too, but stats was pretty different and I only took a couple semesters of it. It wasn't until I was forced to interact with it in a job

I was reasonably mathy too, but stats was pretty different and I only took a couple semesters of it. It wasn't until I was forced to interact with it in a job setting that the inverse relationship between type I & II errors became really apparent to me. Any time I would crank up settings to reduce type I error, keeping all other things the same, type II error would invariably creep up... and v. versa Then when looking in to it, I saw a lot of the examples given from the medical industry as GP alludes to, where this is a real big problem (e.g. if your test is 99.999% accurate, but has a 0.001% false positive rate, then if your disease actually only affects 1/1000000 people then the overwhelming majority of people who your test select are actually healthy). And then I went back and actually read the relevant sections on the stats textbooks for my courses and learned that (a) it was actually in there, and (b) evidently my stats courses were way, way too easy to pass As for the reply, I think I figured out why it was downvoted. It was a double-post, and the intended GGP was here: https://news.ycombinator.com/item?id=16398560 https://news.ycombinator.com/item?id=16398560 Where it makes much more sense.