Estimators are statistical tools used to estimate the parameters of a population based on sample data. Some common types of estimators include: 1. Point estimators: These estimators provide a single best guess for the value of a parameter. Examples include sample mean and sample median. 2. Interval estimators: These estimators provide a range of values within which the true parameter value is likely to lie. Examples include confidence intervals and prediction intervals. 3. Maximum likelihood estimators: These estimators are derived by maximizing the likelihood function, which measures the probability of observing the sample data given a particular parameter value. 4. Bayesian estimators: These estimators incorporate prior knowledge or beliefs about the parameter into the estimation process, typically using Bayes' theorem. 5. Bias-corrected estimators: These estimators adjust for any bias in the point estimate by adding or subtracting a correction factor to get a more accurate estimate of the parameter. Overall, estimators play a crucial role in statistical inference by helping researchers make inferences about population parameters based on sample data.