Answer: One for which the expected value of the estimator is equal to the value of the parameter being estimated

An unbiased estimator is defined as an estimator whose expected value equals the true value of the parameter being estimated. Mathematically, an estimator \(\hat{\theta}\) is unbiased for parameter \(\theta\) if \(E[\hat{\theta}] = \theta\). **Key points:** - Option A describes consistency (an estimator becomes more accurate as sample size increases) - Option B describes efficiency (minimum variance among unbiased estimators) - Option C is incorrect as accuracy typically decreases with smaller sample sizes - Option D correctly defines unbiasedness: the expected value of the estimator equals the parameter value **Example:** The sample mean \(\bar{x}\) is an unbiased estimator of the population mean \(\mu\) because \(E[\bar{x}] = \mu\).

Author: Nikitesh Somanthe