Why Big-O matters
In placement rounds, the interviewer is not only checking if your code works. They are checking if it still works when the input becomes large.
Big-O is the short way to say how your time or memory grows as input size grows. It helps you choose a better approach before you waste time coding.
Key point
One idea
Big-O ignores exact seconds and focuses on growth. Ask: if n doubles, does my work stay almost same, double, or become much worse?
For interviews and your repo, mention both time complexity and space complexity for each solution.