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How are algorithms analyzed? What makes quicksort have an O(n^2) worst-case performance while merge sort has an O(n log(n)) worst-case performance?
That’s a topic for an entire semester. Ultimately we are talking about the upper bound on the number of operations that must be completed before the algorithm finishes as a function of the size of the input. We do not include the coeffecients (ie 10N vs 4N^2) because for N large enough, it doesn’t matter anymore.
How to prove what the big-oh of an algorithm is can be quite difficult. It requires a formal proof and there are many techniques. Often a good adhoc way is to just count how many passes on the data the algorithm makes. For instance, if your algorithm has nested for loops, then for each of N items you must operate N times. That would generally be O(N^2).
As to merge sort, you split the data in half over and over. That takes log2(n). And for each split you make a pass on the data, which gives N log(n).
quick sort is a bit trickier because in the average case it is also n log (n). You have to imagine what happens if your partition splits the data such that every time you get only one element on one side of the partition. Then you will need to split the data n times instead of log(n) times which makes it N^2. The advantage of quicksort is that it can be done in place, and that we usually get closer to N log(n) performance.