1. Unbiased estimation means that when a sample statistic is used to estimate the overall parameters, because the sample statistic is the estimated value of the overall parameters and is influenced by random factors, there must be some errors between the estimated value and the overall parameters. If the average mark-state value of the error is equal to zero, the sample statistics are called unbiased estimators of the population parameters.

2. Specifically, for the unbiased estimator t of the population parameter θ, if E(T)=θ, T is said to be an unbiased estimator of θ. This means that through multiple sampling, we can get a series of estimated values, and the average value of these estimated values should be close to the true value of the overall parameters.

3. The significance of unbiased estimation is that it can provide a relatively reliable and accurate estimation method. Because the average error of unbiased estimation is zero, a relatively stable estimation value can be obtained from multiple sampling, so the true value of the population parameters can be correctly estimated.

The role of unbiased estimation

1 and unbiased estimation are mainly used to estimate the overall parameters accurately. Unbiased estimation is an unbiased inference when using sample statistics to estimate the overall parameters, and the mathematical expectation of the estimator is equal to the true value of the estimated parameters. The significance of unbiased estimation is that their average value is close to the true value of estimation parameters under the number of repetitions, so hail suppression is widely used in test score statistics.

2. The advantage of unbiased estimation is that it can provide a relatively reliable and accurate estimation method. In multiple sampling, we can get a series of estimated values, and the average value of these estimated values should be close to the true value of the overall parameters, so that we can estimate the overall parameters more accurately.

3. The disadvantage of unbiased estimation is that it is not necessarily the best estimation method. Sometimes the variance of unbiased estimation may be large, which leads to the low accuracy of estimation. Therefore, it is necessary to comprehensively consider various factors such as unbiased, variance size and sample size to determine the most suitable estimation method. Not all population parameters have unbiased estimators.