Final answer:
An estimator with an expected value equal to the parameter it is intended to estimate is described as unbiased. This ensures that the estimator's average estimate equals the parameter across many samples, which is essential for accurate statistical inference.
Step-by-step explanation:
An estimator that has an expected value equal to the true parameter it is estimating is described as unbiased. An estimator is unbiased when, in theory, the average of the estimates produced by the estimator from an infinite number of samples equals the parameter being estimated. On the other hand, efficient estimators are those with the smallest possible variance among all unbiased estimators for large sample sizes, while consistent estimators yield results that increasingly converge to the true parameter value as the sample size grows.
For clarification using the provided sample answer context, consider a professor calculating an average exam score for her class. If the average is based on all the scores of the class members, it reflects a parameter. In contrast, an estimator is a statistical measure used when dealing with samples from populations, aiming to infer or estimate the corresponding population parameter. For estimators to be useful, they should ideally be unbiased, as they provide a correct average estimate over many samples.