Parameter estimation is a statistical process used to determine the values of parameters in a mathematical model. These parameters help describe the underlying characteristics of a population based on sample data. For example, in a normal distribution, parameters like the mean and standard deviation are estimated to understand the data's behavior. There are various methods for parameter estimation, including Maximum Likelihood Estimation and Bayesian Estimation. Each method has its own advantages and is chosen based on the specific context of the data and the model being used. Accurate parameter estimation is crucial for making reliable predictions and informed decisions.