Asymptotic analysis is a method used in computer science to evaluate the performance of algorithms. It focuses on how the runtime or space requirements of an algorithm grow as the size of the input increases. This analysis helps in comparing the efficiency of different algorithms, especially for large inputs. In asymptotic analysis, we often use notation like Big O, Big Θ, and Big Ω to describe the upper, tight, and lower bounds of an algorithm's performance. This allows developers to understand the worst-case, average-case, and best-case scenarios, aiding in the selection of the most efficient algorithm for a given problem.