Big-O for Eight Year Olds? - Stack Overflow: "How does this relate to your question about the practical realisations of these bounds? Well, unfortunately, O( ) notation hides constants which real-world implementations have to deal with. So although we can say that, for example, for a T(n^2) operation we have to visit every possible pair of elements, we don't know how many times we have to visit them (except that it's not a function of n). So we could have to visit every pair 10 times, or 10^10 times, and the T(n^2) statement makes no distinction. Lower order functions are also hidden - we could have to visit every pair of elements once, and every individual element 100 times, because n^2 + 100n = T(n^2). The idea behind O( ) notation is that for large enough n, this doesn't matter at all because n^2 gets so much larger than 100n that we don't even notice the impact of 100n on the running time. However, we often deal with 'sufficiently small' n such that constant factors and so on make a real, significant difference.
For example, quicksort (average cost T(n.log n)) and heapsort (average cost T(n.log n)) are both sorting algorithms with the same average cost - yet quicksort is typically much faster than heapsort. This is because heapsort does a few more comparisons per element than quicksort.
This is not to say that O( ) notation is useless,"
