An unbiased estimator A. always captures the true population value of the parameter. B. has no variation due to resampling. C. is necessarily the best estimator, i. e., it achieves the minimum variance. D. captures the true population value of the parameter on average, i. e., the mean of its sampling distribution is the truth. E. cannot be calculated.

C. is necessarily the best estimator, i.e., it achieves the minimum variance.

D. captures the true population value of the parameter on average, i.e., the mean of its sampling distribution is the truth.

Step-by-step explanation:

In statistics, If your data is not an overestimate or underestimate of a population parameter, then your statistic is said to be unbiased.

When an accurate statistic is used to approximate a population parameter it is known as an Unbiased estimator. “Accurate” in this context means it’s neither an overestimate nor an underestimate.

Mathematically, an estimator is said to be unbiased if: <Ô> = Ø

The above equation means that when the sample mean equals the population mean then it is unbiased estimator.

However, If an overestimate or underestimate occurs, the mean of the difference is called a “bias.”

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