In statistics, the mean squared error (MSE)[1][2] or mean squared deviation (MSD) of an estimator (of a procedure for estimating an unobserved quantity) measures the average of the squares of the errors—that is, the average squared difference between the estimated values and the actual value. (more detail: https://actruce.com/bias-mse-point-estimator/)
Difference between Variance and MSE
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💡 when the bias of the estimator is zero, the variance and mean squared error are equal.