I have watched Andrew Ng's lecture Error Analysis and the first slide of the lecture says:
Error analysis: Manually examine the examples (in cross validation set) that your algorithm made errors on. See if you spot any systematic trend in what type of examples it is making errors on.
I believe that the term "cross validation set" here has nothing to do with cross validation technique as he has never mentioned it before this lecture, so the "cross validation set" here is just a normal validation set if I'm not misunderstanding (or maybe "cross validation set" = "validation set"? I'm a newbie to Machine Learning by the way...).
My main question is: Doesn't that make me do data snooping on the validation set, which will make the validation error of the hypothesis with least validation error much less accurate than it should be?