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.