* That constant may be so large that an O(N^2) may be a better solution for any practical purpose.* Another example of this is that a good system/library sorti

* That constant may be so large that an O(N^2) may be a better solution for any practical purpose.* Another example of this is that a good system/library sorting function will sometimes use an asymptotically fast algorithm like quicksort or mergesort only for arrays over a certain size, but will switch over to a O(n^2) algorithm like insertion sort to sort smaller arrays, because it has smaller constants and coefficients. (But sometimes this is overkill, because if n is small, you probably aren't going to notice the speed difference anyway.) So yes, Big O is all about asymptotic behavior, which basically means, the rate of growth as n approaches infinity. Because a large value of n is when the choice of algorithm starts to really matter for performance.