People say it takes amortized O(1) to put into a hash table. Therefore, putting n elements must be O(n). That’s not true for large n, however, since as an answerer said, “All you need to satisfy expected amortized O(1) is to expand the table and rehash everything with a new random hash function any time there is a collision.”
So: what is the average running-time of inserting n elements into a hash table? I realize this is probably implementation-dependent, so mention what type of implementation you’re talking about.
For example, if there are (log n) equally spaced collisions, and each collision takes O(k) to resolve, where k is the current size of the hashtable, then you’d have this recurrence relation:
T(n) = T(n/2) + n/2 + n/2
(that is, you take the time to insert n/2 elements, then you have a collision, taking n/2 to resolve, then you do the remaining n/2 inserts without a collision). This still ends up being O(n), so yay. But is this reasonable?
It completely depends on how inefficient your rehashing is. Specifically, if you can properly estimate the expected size of your hashtable the second time, your runtime still approaches O(n). Effectively, you have to specify how inefficient your rehash size calculation is before you can determine the expected order.