 The bias depends both on the sampling distribution of the estimator and on the transform, and can be quite involved to calculate – see unbiased estimation of standard deviation for a discussion in this case.While bias quantifies the "average" difference to be expected between an estimator and an underlying parameter, an estimator based on a finite sample can additionally be expected to differ from the parameter due to the randomness in the sample.One measure which is used to try to reflect both types of difference is the mean square error,

This can be shown to be equal to the square of the bias, plus the variance