Maximum likelihood estimation (MLE) is a popular statistical method used for fitting a mathematical model to data. The modeling of real world data using estimation by maximum likelihood offers a way of tuning the free parameters of the model to provide a good fit. The method was pioneered by Sir Ronald A. Fisher between 1912 and 1922. For a fixed set of data and underlying probability model, maximum likelihood picks the values of the model parameters that make the data "more likely" than any other values of the parameters would make them. Maximum likelihood estimation gives a unique and easy way to determine solution in the case of the normal distribution and many other problems.