This is the seventh in a series of videos about using Big O notation to describe the complexity of an algorithm. That is, how the performance of an algorithm varies according to the amount of input data. This particular video looks at the time complexity, and space complexity, of three well known algorithms, namely the linear search, the quicksort and the merge sort. The influence of the call stack on the Big O space complexity of a recursive algorithm is examined. This video assumes that you are already familiar with the workings of the linear search, the quicksort and the merge sort (which are covered in detail in other videos) and that you have already met some Big O time complexities.