1. The theory of estimation in statistics is about making inferences about population parameters based on sample data. 2. Key concepts include point estimation (estimating a parameter by a single value), interval estimation (giving a range within which the parameter likely lies), and properties of estimators such as unbiasedness, consistency, and efficiency. 3. For example, the sample mean is a point estimator of the population mean and is unbiased because its expected value equals the true mean. 4. An estimator \(\hat{\theta}\) is unbiased if \(E[\hat{\theta}] = \theta\), consistent if \(\hat{\theta} \to \theta\) as sample size increases, and efficient if it has the smallest variance among unbiased estimators. 5. In practice, estimation theory helps us construct confidence intervals and test hypotheses about parameters using sample data.