I ' ve used 1000 as the upper bound because you claim that big O only matters for ' massive ' numbers like 1000 I don ' t think this is a massive number but let ' s ignore that . No , you said massive . I put ' being massive ' in quotes to indicate I was referring to what you ' d said . I said we should care about Big Oh for input values as small as 1000 . Holy shit , you can ' t make any argument without misconstruing things . And again , your graph ' s bounds completely hide what ' s really going on . This is much better ~ jmarshallcourses 2002 springcs 50 BigOsorting . gif With an input size of 100 , n log n takes about 4 times longer . My entire point boils down to the fact that because logn stays relatively small compared to nIt doesn ' t , they are very different , which is obvious if you look at a graph with reasonable bounds or just look at a chart of log n , n , and nlogn as n grows . Onlogn has similar , but not exact , growth to On . No , it ' s not similar , see the graph I showed you . Something taking 4 times as long for an input size of only 100 is not similar .