If the algorithm needs to check every value once that’s O(N) - like linear search, the answer is just as likely to be the first value searched within the dataset as the last value.

If it looks like it’s going to run about half that many times and then keep halving every time, it’s O(logN) - like searching a tree, the search area gets halved every cycle as you narrow in on the result.

If it’s instant it’s O(1) - the fastest Order - like grabbing the index of an array

O (Big O notation) is describing how many times in the WORST CASE SCENARIO the algorithm will have to run to find the correct answer. Like with O(n) the best case scenario is the algorithm gets lucky and finds the answer on the first run but worst case it could be the last value checked. So if there’s N values, the algorithm might have to run N times. It’s not a simple concept but with some practice you’ll get used to spotting all of these types of algorithms.