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Estimators are fundamental tools in statistics, used to approximate unknown population parameters based on sample data. The goal of an estimator is to provide a single value, or a range of values, that is likely to be close to the true parameter. For example, the sample mean is a common estimator for the population mean. The quality of an estimator is judged by its properties, such as unbiasedness, efficiency, and consistency. An unbiased estimator is one whose expected value is equal to the true parameter value. Efficiency refers to how close the estimator's values tend to be to the true parameter. A consistent estimator is one that converges to the true parameter value as the sample size increases. Different estimators exist for different parameters and situations. Maximum likelihood estimation and method of moments are two common approaches for constructing estimators. The choice of estimator can significantly impact the conclusions drawn from statistical analysis.