Mutual information (MI) is a quantity that measures the dependence between two arbitrary random variables and has been repeatedly used to solve a wide variety of bioinformatic problems. Recently, when attempting to quantify the effects of sampling variance on computed values of MI in proteins, we encountered striking differences among various novel estimates of MI. These differences revealed that estimating the true value of MI is not a straightforward procedure, and minor variations of assumptions yielded remarkably different estimates.