I have noticed in my short academic life that many published papers in our area sometimes do not have much rigor regarding statistics. This is not just an assumption; I have heard professors say the same.
For example, in CS disciplines I see papers being published claiming that methodology X has been observed to be effective and this is proved by ANOVA and ANCOVA, however I see no references for other researchers evaluating that the necessary constraints have been observed. It somewhat feels like as soon as some 'complex function and name' appears, then that shows the researcher is using some highly credible method and approach that 'he must know what is he doing and it is fine if he does not describe the constraints', say, for that given distribution or approach, so that the community can evaluate it.
Sometimes, there are excuses for justifying the hypothesis with such a small sample size.
My question here is thusly posed as a student of CS disciplines as an aspirant to learn more about statistics: How do computer scientists approaches statistics?
This question might seems like I am asking what I have already explained, but that is my opinion. I might be wrong, or I might be focusing on a group of practitioners whereas other groups of CS researchers might be doing something else that follows better practices with respect to statistics rigor.
So specifically, what I want is "Our area is or is not into statistics because of the given facts (papers example, books, or another discussion article about this are fine)". @Patrick answer is closer to this.