Principles and procedures of exploratory data analysis

Exploratory data analysis (EDA) is a well-established statistical tradition that provides conceptual and computational tools for discovering patterns to foster hypothesis development and refinement. These tools and attitudes complement the use of significance and hypothesis tests used in confirmatory data analysis (CDA). Although EDA complements rather than replaces CDA, use of CDA without EDA is seldom warranted. Even when well specified theories are held, EDA helps one interpret the results of CDA and may reveal unexpected or misleading patterns in the data. This article introduces the central heuristics and computational tools of EDA and contrasts it with CDA and exploratory statistics in general. EDA techniques are illustrated using previously published psychological data. Changes in statistical training and practice are recommended to incorporate these tools.