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Answer: The bias is the difference between the expected value of an estimator and the true value of the population parameter.
**Correct Answer: D** **Explanation:** D is correct. The bias of an estimator θ̂ is defined as: Bias(θ) = E(θ̂) − θ Where θ is the true value of the parameter that we are estimating. It measures the difference between the expected value of the estimator and the population value being estimated. A and C are incorrect. As explained above, bias is calculated as the difference between the expected value of the estimator and the population value being estimated. B is incorrect. When the expected value of the estimator is equal to the true population value being estimated, bias is zero.
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Which of the following correctly describes the bias of an estimator?
A
The bias is typically calculated by taking the square of the deviation between two mean estimators.
B
The bias is greatest when the expected value of the estimator is equal to the true population value being estimated.
C
The bias is equal to the expected value of the estimator divided by the population value being estimated.
D
The bias is the difference between the expected value of an estimator and the true value of the population parameter.
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