Exploratory data analysis (EDA) is a term first utilized by John Tukey (1977), and is intended to contrast with the more traditional statistical approach to data analysis that starts with hypothesis testing and model building. Instead of using confirmatory data analysis (CDA) methods to verify or refute suspected hypotheses, Tukey (1977) advocated the use of basic descriptive statistics and visualization methods to generate information that would lead to the development of hypotheses to test. The objectives of EDA (see Velleman and Hoaglin 1981) are therefore to provide statistical summaries of the data as a pre-processing step before building a model or testing hypotheses. The EDA phase enables new hypotheses to be suggested about the causes of the relationships in the data, and provides an opportunity to determine whether the assumption that various models rely upon are valid for the particular dataset. It provides us with a very rapid feel for the data: the shape of its...
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References and Further Reading
Borg I, Groenen PJF (2005) Modern multidimensional scaling: theory and applications. Springer-Verlag, New York
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Velleman PF, Hoaglin DC (1981) The ABC’s of EDA. Duxbury Press, Boston, MA
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Smith-Miles, K. (2011). Exploratory Data Analysis. In: Lovric, M. (eds) International Encyclopedia of Statistical Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04898-2_242
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