## Complete Estimator :

A complete estimator is a statistical method that provides an unbiased estimate of a population parameter. This means that the estimator consistently produces estimates that are equal to the true population value, without any systematic bias. In other words, a complete estimator is a reliable and accurate tool for estimating population characteristics.

One example of a complete estimator is the sample mean, which is used to estimate the population mean. The sample mean is calculated by summing up all the observations in a sample and dividing by the sample size. For example, if we have a sample of 10 people and their ages are 25, 30, 35, 40, 45, 50, 55, 60, 65, and 70, the sample mean would be calculated as follows:

Sample mean = (25 + 30 + 35 + 40 + 45 + 50 + 55 + 60 + 65 + 70) / 10 = 475 / 10 = 47.5

This sample mean is an unbiased estimate of the population mean, because it is calculated using all the observations in the sample and it consistently produces estimates that are equal to the true population value.

Another example of a complete estimator is the sample median, which is used to estimate the population median. The sample median is calculated by arranging the observations in a sample in numerical order and then selecting the middle observation. For example, if we have a sample of 10 people and their ages are 25, 30, 35, 40, 45, 50, 55, 60, 65, and 70, the sample median would be calculated as follows:

Sample median = (35 + 40) / 2 = 75 / 2 = 37.5

This sample median is an unbiased estimate of the population median, because it is calculated using the middle observation in the sample and it consistently produces estimates that are equal to the true population value.

In summary, complete estimators are statistical methods that provide unbiased estimates of population parameters. They are reliable and accurate tools for estimating population characteristics, and they consistently produce estimates that are equal to the true population value. Examples of complete estimators include the sample mean and the sample median.