PRINCIPLES AND PROCEDURES OF EXPLORATORY DATA-ANALYSIS

Exploratory data analysis (EDA) is a well-established statistical trad ition 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 ra ther than replaces CDA, use of CDA without EDA is seldom warranted. Ev en 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 computationa l tools of EDA and contrasts it with CDA and exploratory statistics in general. EDA techniques are illustrated using previously published ps ychological data. Changes in statistical training and practice are rec ommended to incorporate these tools.