This point should be understood very clearly: The article isn't wrong that an ArrayList has O(n) for a single insertion. However, the article misses that amorti

This point should be understood very clearly: The article isn't wrong that an ArrayList has O(n) for a single insertion. However, the article misses that amortized analysis shows that the same structure can be made to have O(n) for n insertions as well. In effect, ArrayLists can be thought to have O(1) insertion time in practice meaning that all you are comparing are the differences in constants between the two structures. I think the biggest takeaway from this isn't that big-O can fool you (which it very well can), but that misunderstanding analysis can lead to poor decisions. A better argument for big-O fooling you can be found using Quicksort and any proper O(n Log(n)) sorting algorithm. Quicksort will outperform most in practical cases, but is technically O(n^2) in the worst case (average case O(n Log(n)).