Estimation Theory is a branch of statistics that focuses on estimating the parameters of a statistical model based on observed data. It provides methods to derive estimates that are as close as possible to the true values, often using techniques like maximum likelihood estimation or Bayesian estimation. These methods help in making informed decisions in various fields, including engineering, economics, and social sciences. The theory also addresses the accuracy and reliability of these estimates through concepts like bias, variance, and confidence intervals. By understanding these concepts, researchers can evaluate how well their estimates represent the underlying reality, leading to better predictions and analyses.