Regardless of the method of analysis, the most common way to summarize the performance of an algorithm is through Big O notation. Big O notation describes the growth rate of the time or space an algorithm requires as the problem increases in size. What do we mean by the size of the problem? Intuitively we mean the number of things it involves. In a payroll system the size might be the number of employees, in a general database system, the number of records in the system, for Google the number of web pages in the world. More concretely, for our current problem, searching a list, it is best measured by the number of items in the list, n.