Big O in probability notation

In the context of algorithm analysis, Big O notation is typically used to describe the upper bound of an algorithm's time or space complexity in terms of its input size. It provides a way to characterize how the runtime or space requirements of an algorithm grow as the size of the input increases. In probability and statistics, while Big O notation is not as commonly used as in algorithm analysis, it can also be applied to describe the growth rates of random variables or functions of random variables under certain conditions.