[roel_v]

Well that's not what your article is saying - it says (well, not in these words, but it's at least what I got from it) that it's meant to be something practical to understanding algorithmic complexity and how that relates to code performance. And for that, the size of the data and the details of the algorithm very much do matter. Throughout these comments, people seem to be using two 'concepts' of big O and because of that, talking past each other: the academic 'provable upper boundary' concept and the applied 'what algorithm or data structure should I choose for my concrete problem, and how does complexity help me decide' concept. That last one is what is colloquially known as 'big O analysis', whereas technically that term is reserved for something else indeed. I'll readily admit that when I first made my GP comment, I didn't really clearly make that distinction in my mind either, which is probably what is the real underlying issue I was trying to point out.