The article explains how shrinking estimators can improve accuracy by reducing variance, even though they introduce bias. It starts by comparing the sample mean, zero estimator, and oracle estimator for estimating a single mean. Then, it introduces the James-Stein estimator, which combines shrinking coefficients with canceling out overestimates and underestimates to provide more accurate estimates.