exploratory data analysis

Approach of analyzing data sets in statistics

Entityfield of study field of studyQ1322871

Also known as EDA

Instance of: field of study
, field of study
Subclass of: analytical chemical technique
Part of: statistical data interpretation

Overview

From Wikipedia, the free encyclopedia · Read full article · Text CC BY-SA 4.0

In statistics, exploratory data analysis (EDA) or exploratory analytics is an approach of analyzing data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods. A statistical model 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 and tested. Exploratory data analysis has been promoted by John Tukey 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 initial data analysis (IDA), 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.

Overview

All data

14 statements · 29% with external sources · numbers in brackets are citations

ACM Classification Code (2012): 10003699

On other databases

9 identifiers linking exploratory data analysis across the web's reference databases

Data quality

statements

29%

externally sourced

Source last edited July 10, 2026. AIDB refreshes this page daily.

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AIDB. (2026). exploratory data analysis (Q1322871). Retrieved from https://aidb.si/e/exploratory-data-analysis-Q1322871