This chapter introduces the notion of exploratory data analysis (EDA) which is a key component of data science and an important pre-requisite for statistics.
EDA comprises one of the most important parts of data analysis — and one that is often ignored or under-estimated in typical courses on statistics. Especially when subscribing to the principles of transparent data analysis and reproducible research (e.g., by sharing analysis notebooks in R Markdown, see Appendix F), a solid EDA allows establishing consensus about the main characteristics of a dataset. While such practices are indispensable when working in a team of colleagues and the wider scientific community, organizing our workflow in a clear and consistent fashion is also beneficial for our own projects and our future self.
Although any actual EDA is tailored to specific features of a dataset and the current research goals, this session highlights some common themes that are relevant in most cases. We illustrate the essential steps of an EDA by exploring a real dataset from clincial psychology (Woodworth et al., 2018), which measures the effects of positive psychology interventions (see Section B.1 of Appendix B for details on the data).