The idea behind Big-O notation is how the time generally varies as you increase the number of items for a given algorithm on a given data structure/data set. Th

The idea behind Big-O notation is how the time generally varies as you increase the number of items for a given algorithm on a given data structure/data set. That is if you plot a graph `y=f(x)` where `f(x)` is the time taken to perform that operation and x is the number of items you are performing it on. You can then match that resulting curve to a polynomial or other mathematical expression, and Big-O is the dominant term in that expression without any associated scale factors (e.g. for 6x^3 + 2x^2 + 7 you have an O(n^3) algorithm). Sure, you can choose a large constant such that numerically it is equal to or smaller than O(n^2) for a given n, but as you vary n then O(1) should approximate a flat `y=N` line, while O(n^2) should approximate a parabola and would result in values larger and smaller than N as you vary it.