# Bayesian data analysis in empirical software engineering---The case of missing data

## TL;DR

This paper introduces Bayesian data analysis (BDA) to empirical software engineering, demonstrating its application to handle missing data and improve statistical robustness, aiming to enhance reproducibility in the field.

## Contribution

It provides a detailed example of applying BDA to address missing data in empirical software engineering, including steps from problem understanding to sensitivity analysis.

## Key findings

- BDA effectively manages missing data in software engineering studies.
- Bayesian methods improve the robustness and reproducibility of statistical analysis.
- Diagnostics and sensitivity analyses are crucial for validating Bayesian models.

## Abstract

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.