A consistent estimator converges to the true parameter as sample size grows. With enough data, the estimate settles on the right value in probability. Consistency is why larger samples help even when a single estimate is noisy. Do not confuse consistency with unbiasedness; an estimator can be biased in finite samples yet still consistent.