Big O is just complexity classes, describing how number of abstract computations scale (that's the key word) with input size (input list length). Generally speaking, if you could analytically express number of computations as a function of input size, Big O takes the largest term and drops all factors.
It does not necessarily describe performance of the algorithm. `20n2^+5n` and `2n^2 + 9001n` are both O(n^2)
It does not necessarily describe performance of the algorithm. `20n2^+5n` and `2n^2 + 9001n` are both O(n^2)