Following on the heels of the chapter on nonlinear estimation, this chapter focusses on some of the common pitfalls and failure modes of estimation techniques. We begin by discussing some key properties that we would like healthy estimators to have (i.e., unbiased, consistent) and how to measure these properties. We delve more deeply into biases and discuss how in some cases we can fold bias estimation right into our estimator, while in other cases we cannot. We touch briefly on data association (matching measurements to the right parts of models) and how to mitigate the effect of outlier measurements using robust estimation. We close with some methods to determine good measurement covariances for use in our estimators.