Western Governors University (WGU) ICSC2211 D684 Introduction to Computer Science Practice Test

The most efficient algorithm for large datasets that are already sorted is Merge Sort. Merge Sort operates with a time complexity of O(n log n) in the average and worst cases, making it consistently efficient, regardless of the initial state of the data.

If the input data is already sorted, Merge Sort still maintains its O(n log n) performance since it doesn't take advantage of the data being sorted in the way some other algorithms might. This characteristic allows it to handle large datasets effectively, particularly in cases where stable sorting is required, as it preserves the original order of equal elements.

In contrast, Bubble Sort has a time complexity of O(n²) in the average and worst-case scenarios, making it inefficient for large data sets. Insertion Sort can take advantage of partially sorted data, resulting in a time complexity of O(n) in the best case when the data is already sorted, but it still doesn't match the efficiency of Merge Sort for larger datasets overall. Selection Sort also has a time complexity of O(n²) in both average and worst cases, making it similarly not ideal for large datasets.

Overall, Merge Sort stands out as the most suitable choice for efficiently handling large datasets, even if they are sorted.