Exploratory data analysis

<p>In <a href="/facts/Statistics/DNPsKGYU">statistics</a>, exploratory data analysis (EDA) is an approach of <a href="/facts/Data_analysis/CNtARxLW">analyzing</a> <a href="/facts/Data_set/eDvegUTK">data sets</a> to summarize their main characteristics, often using <a href="/facts/Statistical_graphics/SAo9EjFx">statistical graphics</a> and other <a href="/facts/Data_visualization/ET1lSjFa">data visualization</a> methods. A <a href="/facts/Statistical_model/jKkT5ftm">statistical model</a> can be used or not, but primarily EDA is for seeing what the data can tell beyond the formal modeling and thereby contrasts with traditional hypothesis testing, in which a model is supposed to be selected before the data is seen. Exploratory data analysis has been promoted by <a href="/facts/John_Tukey/2a5FbPWb">John Tukey</a> since 1970 to encourage statisticians to explore the data, and possibly formulate hypotheses that could lead to new data collection and experiments. EDA is different from <a href="/facts/Data_analysis/CNtARxLW">initial data analysis (IDA)</a>, which focuses more narrowly on checking assumptions required for model fitting and hypothesis testing, and handling missing values and making transformations of variables as needed. EDA encompasses IDA.
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Exploratory data analysis open-in-new

Exploratory data analysis