Why is a small MSE (mean square error) value favoured in statistics and what problems does it solve?
“In statistics, the mean squared error or MSE of an estimator is the expected value of the square of the “error.” The error is the amount by which the estimator differs from the quantity to be estimated. The difference occurs because of randomness or because the estimator doesn’t account for information that could produce a more accurate estimate.” The smaller the mean squared error is, the closer the estimator is to the actual data.
