What is Big O notation and why is it used?

Big O notation describes the upper bound of an algorithm's growth rate, representing worst-case scenario in terms of input size n. Common complexities from best to worst: O(1) constant, O(log n) logarithmic, O(n) linear, O(n log n) linearithmic, O(n^2) quadratic, O(2^n) exponential. It abstracts away constants and lower-order terms, allowing comparison of algorithmic efficiency regardless of hardware.