An O(n log n) sort applied to a nearly-sorted list can be slower than an O(n²) insertion sort on the same data. Why does the input's shape overturn the growth classes?

B. Because O(n²) is its worst case, and nearly-sorted data approaches O(n).

Insertion sort's quadratic figure is its worst case: on sorted data each element is compared once and stays put, where merge sort does its full work regardless of input. So the comparison depends on which case the input puts you in. Quicksort is the mirror image, quadratic on sorted input with a naive pivot, and Timsort detects existing runs precisely to claim insertion sort's best case.