Time complexity analysis helps predict how efficiently a solution will run, especially on large inputs, and avoids solutions that exceed time limits.

Time complexity analysis is fundamental in competitive programming because it helps programmers evaluate whether their solution will execute within the given time limits, particularly on large inputs. Time complexity, represented with Big-O notation (e.g., O(n), O(n log n), or O(n^2)), estimates the number of operations a program will perform relative to the input size. For instance, linear time complexity O(n) is often acceptable, but quadratic time O(n^2) or exponential time O(2^n) can be too slow for larger inputs. Understanding time complexity enables programmers to make educated decisions about which algorithms or data structures to use based on the problem's constraints. For example, binary search operates in O(log n) time, making it ideal for quickly finding elements in sorted arrays. Likewise, algorithms like sorting, searching, or graph traversal have different time complexities, and analyzing them helps identify which approach best balances speed and accuracy. Mastering time complexity also helps avoid common issues like TLE (Time Limit Exceeded), which occurs when solutions are too slow. By practicing with diverse algorithms and complexities, programmers can improve their problem-solving efficiency and perform well in competitive programming environments.