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Constant Time vs. Linear Time

Now that we’ve encountered O(N), we can begin to see that Big O Notation does more than simply describe the number of steps that an algorithm takes, such as a hard number such as 22 or 400. Rather, it describes how many steps an algorithm takes based on the number of data elements that the algorithm is acting upon. Another way of saying this is that Big O answers the following question: how does the number of steps change as the data increases?

An algorithm that is O(N) will take as many steps as there are elements of data. So when an array increases in size by one element, an O(N) algorithm will increase by one step. An algorithm that is O(1) will take the same number of steps no matter how large the array gets. ...

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