I’m trying to understand the practical implications of time complexity on the performance of sorting algorithms like Bubble Sort, Merge Sort, and Quick Sort. While I know the theoretical time complexities (O(n²) for Bubble Sort, O(n log n) for Merge and Quick Sort), I’m unclear on how these complexities translate to actual performance in different scenarios. Are there specific cases where Bubble Sort could outperform the others, despite its higher time complexity?

I’ve read through Wikipedia articles and other resources on sorting algorithms and their time complexities, but I’m still struggling to grasp how these theoretical differences play out in practice. I haven’t run any benchmarks yet but am curious if others have experience or data that could clarify this. I was expecting to find clear guidelines or case studies where one sorting algorithm might be preferable over another, but the information I found was mostly theoretical.

I’m particularly interested in scenarios where the input data size, structure, or specific use cases might make one algorithm more efficient than another, even if its time complexity suggests otherwise. Should I focus on running benchmarks with my specific data to get a more accurate understanding, or are there general rules of thumb that apply?