How is the unbiasness of estimators defined?
A)An estimator is unbiased if its expected value equals the estimated population parameter.
B)An unbiased estimator approaches the estimated population parameter as the sample size grows larger.
C)An estimator is unbiased if it always gives the correct value of the estimated population parameter.
D)An estimator is unbiased if its standard error is lower than that of other estimators of the estimated population parameter.

Question

Quizplus · Accepted Answer

Unbiasedness of an estimator is defined as the property that its expected value equals the estimated population parameter. In other words, on average, the estimator is not systematically overestimating or underestimating the population parameter. Option A correctly defines this property. Option B refers to the consistency of an estimator, rather than unbiasedness. Option C is unrealistic as even the best estimator will not always give the correct value of the population parameter. Option D refers to the precision of an estimator, rather than unbiasedness.

How Is the Unbiasness of Estimators Defined