An unbiased estimator is said to be consistent if the difference between the estimator and the parameter grows smaller as the sample size grows larger.

This statement accurately describes the concept of consistency for an estimator. Consistency means that as the sample size increases, the estimator becomes increasingly accurate, and the difference between the estimated value and the true value of the parameter approaches zero. This is an important property for an estimator to have, as it ensures that with enough data, the estimator will converge on the true value of the parameter.