  Bayesian data analysis (BDA) is today used by a multitude of research
disciplines. These disciplines use BDA as a way to embrace uncertainty by using
multilevel models and making use of all available information at hand. In this
chapter, we first introduce the reader to BDA and then provide an example from
empirical software engineering, where we also deal with a common issue in our
field, i.e., missing data.
  The example we make use of presents the steps done when conducting state of
the art statistical analysis. First, we need to understand the problem we want
to solve. Second, we conduct causal analysis. Third, we analyze
non-identifiability. Fourth, we conduct missing data analysis. Finally, we do a
sensitivity analysis of priors. All this before we design our statistical
model. Once we have a model, we present several diagnostics one can use to
conduct sanity checks.
  We hope that through these examples, the reader will see the advantages of
using BDA. This way, we hope Bayesian statistics will become more prevalent in
our field, thus partly avoiding the reproducibility crisis we have seen in
other disciplines.
