sec searching unsorted and sorted arrays it is not always practical to reduce an algorithm' growth rate there is practicality window for every problemin that we have practical limit to how big an input we wish to solve for if our problem size never grows too bigit might not matter if we can reduce the cost by an extra log factorbecause the constant factors in the two algorithms might overwhelm the savings for our two algorithmslet us look further and check the actual number of comparisons used for binary searchwe need about log total comparisons quadratic binary search requires about lg lg comparisons if we incorporate this observation into our tablewe get different picture about the relative differences lg lg lg difference worse same but we still are not done this is only count of comparisonsbinary search is inherently much simpler than qbsbecause binary search only needs to calculate the midpoint position of the array before each comparisonwhile quadratic binary search must calculate an interpolation point which is more expensive so the constant factors for qbs are even higher not only are the constant factors worse on averagebut qbs is far more dependent than binary search on good data distribution to perform well for exampleimagine that you are searching telephone directory for the name "young normally you would look near the back of the book if you found name beginning with ' ,you might look just little ways toward the front if the next name you find also begins with ' ,you would look little further toward the front if this particular telephone directory were unusual in that half of the entries begin with ' ,then you would need to move toward the front many timeseach time eliminating relatively few records from the search in the extremethe performance of interpolation search might not be much better than sequential search if the distribution of key values is badly calculated while it turns out that qbs is not practical algorithmthis is not typical situation fortunatelyalgorithm growth rates are usually well behavedso that asymptotic algorithm analysis nearly always gives us practical indication for which of two algorithms is better