This paper considers the buffer insertion problem under process variations. With continued technology scaling, it is necessary to model the physical parameters to be random variables. One approach to the buffer insertion problem under variations is to use the mean values of these parameters and solve the problem using traditional buffer insertion techniques for delay minimization. Another approach is to find a buffer insertion solution using a new method that can handle the probability distributions. Thus, the performance can be optimized with some yield constraint. In this paper, we present both analytical and experimental results to show that the two approaches give almost identical solutions. In other words, the more expensive statistical methods are not needed for the buffer insertion in delay minimization problem.