Summarize time complexity of merge sort (#20632)

Add simple way to remember time complexity of merge sort

guide/english/algorithms/sorting-algorithms/merge-sort/index.md

@@ -21,7 +21,7 @@ T(n) = 2T(n/2) + n
      = n + n + ... + n + n + n
 ```
      
-Counting the number of repetitions of n in the sum at the end, we see that there are lg n + 1 of them.  Thus the running time is n(lg n + 1) = n lg n + n. We observe that n lg n + n < n lg n + n lg n = 2n lg n for n>0, so the running time is O(n lg n).     
+Counting the number of repetitions of n in the sum at the end, we see that there are lg n + 1 of them.  Thus the running time is n(lg n + 1) = n lg n + n. We observe that n lg n + n < n lg n + n lg n = 2n lg n for n>0, so the running time is O(n lg n). Another simple way to remember the time complexity O(n lg n) of merge sort is that it takes roughly log<sub>2</sub><sup>n</sup> steps to split an array of size n to multiple arrays of size one. After each split, the algorithm have to merge 2 sorted arrays into one which can take n steps in total. As a result, the time complexity for merge sort is O(n lg n).
 
 ```Algorithm
 MergeSort(arr[], left,  right):