Explain the significance of the "Big-O" notation in analyzing the time complexity of algorithms, and provide an example where the difference between O(n) and O(n log n) impacts the choice of algorithm.

Big-O notation describes the upper bound of an algorithm’s time complexity, providing a measure of its efficiency as input size grows. For example, a sorting algorithm like merge sort with O(n log n) complexity is generally preferred over bubble sort with O(n^2) for large datasets, as the former scales more efficiently with increasing input sizes, reducing processing time significantly.