Can you explain what the Big O notation in algorithm analysis means?

Big O notation is used in algorithm analysis to describe the asymptotic complexity — that is, how the running time or memory usage grows depending on the size of the input data.

For example, if an algorithm has a complexity of O(n), it means that its running time grows linearly with the increase in input size n. If it is O(n²), the time grows proportionally to the square of the input size.

This helps compare algorithms in terms of efficiency, especially with large data volumes, ignoring constants and less significant terms.