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. The widespread availability of software for graphical data analysis and calls for increased use of exploratory data analysis (EDA) on epistemic grounds (e.g. Cohen, 1994) have increased the visibility of EDA. Nevertheless, few psychologists receive explicit training in the beliefs or procedures of this tradition. Huberty (1991) remarked that statistical texts are likely to give cursory references to common EDA techniques such as stem-and-leaf plots, box plots, or residual analysis and yet seldom integrate these techniques throughout a book. A survey of graduate training programs in psychology corroborates such an impression