Principles and procedures of exploratory data analysis.

doi:10.1037/1082-989X.2.2.131

Paper

Principles and procedures of exploratory data analysis.

Published Jun 1, 1997 · J. Behrens

Psychological Methods

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Abstract

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.

Study Snapshot

Key takeawayExploratory data analysis complements confirmatory data analysis and helps interpret results, revealing unexpected or misleading patterns in data.

PopulationOlder adults (50-71 years)

Sample size24

MethodsObservational

OutcomesBody Mass Index projections

ResultsSocial networks mitigate obesity in older groups.

Principles and procedures of exploratory data analysis.

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