Exploratory Data Analysis: Getting to Know Your Data

In broad terms, Exploratory Data Analysis (EDA) can be defined as the numerical and graphical examination of data characteristics and relationships before formal, rigorous statistical analyses are applied. Although the temptation to omit EDA in favor of delving into ANOVAs, MANOVAs and the like is great, the role of EDA cannot be underemphasized. EDA rewards the user with a better understanding of the intricacies of the relationships among data. In addition, EDA can be used to probe the validity of assumptions that are made by formal statistical tests (eg., normally distributed data/residuals, homogeneity of variances, additivity, etc.) EDA can be envisioned as the setup for more formal analyses. It provides all the prerequisites for a final, no-surprises formal analysis of the data.