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Exploratory Data Analysis

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Exploratory data analysis (EDA) is an approach to data analysis that employs a number of different techniques to:

  1. 1.

    Look at data to see what it seems to say.

  2. 2.

    Uncover underlying structures.

  3. 3.

    Isolate important variables.

  4. 4.

    Detect outliers and other anomalies.

  5. 5.

    Suggest suitable models for conventional statistics.

Key Points

The term “exploratory data analysis” was introduced by John W. Tukey who in [2] shows how simple graphical and quantitative techniques can be used to open-mindedly explore data.

Typical graphical techniques are:

  1. 1.

    Plotting the raw data (e.g., stem-and-leaf diagrams, histograms, scatter plots)

  2. 2.

    Plotting simple statistics (e.g., mean plots, box plots, residual plots)

  3. 3.

    Positioning (multiple) plots to amplify cognition

Typical quantitative techniques are:

  1. 1.

    Interval estimation

  2. 2.

    Measures of location or of scale

  3. 3.

    Shapes of distributions

Exploratory data analysis can help to improve the results of statistical hypothesis testing by forcing one to...


  • Exploratory Data Analysis (EDA)
  • Confirmatory Data Analysis
  • Conventional Conditions
  • Validation Stage
  • Plot Means

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Recommended Reading

  1. Berry MJA, Linoff GS. Mastering data mining. New York: Wiley; 2000.

    Google Scholar 

  2. Tukey JW. Exploratory data analysis. Reading: Addison Wesley; 1977.

    Google Scholar 

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Correspondence to Hans Hinterberger .

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Hinterberger, H. (2017). Exploratory Data Analysis. In: Liu, L., Özsu, M. (eds) Encyclopedia of Database Systems. Springer, New York, NY.

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