# Is the usage for asymptotic notation for these algorithms correct? [duplicate] So after reading a lot of information around asymptotic analysis of algorithms and the use of Big O / Big Ω and Θ, I’m trying to grasp how to utilise this in the best way when representing algorithms and operations on data structures.

For example there is a recommended website where I got this screenshot from describing Quicksort and I’ve noticed a few issues that stand out to me based on what I’ve learnt.

1. Is it possible for all notations to represent “Best” “Average” and “Worst” cases? and if so how is this possible? For example for a “Worst” case, How can Big Ω represent the Upper bound. The upper bound is tied to Big O.
2. I thought in order to find Theta Θ, Big O and Big Ω had to be the same values? In the screenshot “Best” case is `n log(n)` and Worst case is `n^2` so how can `Θ(n log(n))`?
3. Take for instance a Hash Table data structure, if you were to perform an analysis on the time complexity for insertion of an element. Would I be correct is saying you could interchangeably say `Ω(1)` and `O(N)` or conversely “Average Case is O(1)” and “Worst Case is O(N)”?  Posted on Categories proxies