Explanatory Data Analysis (EDA) in statistics is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task. Exploratory data analysis was promoted 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. Exploratory data analysis, robust statistics, nonparametric statistics, and the development of statistical programming languages facilitated statisticians’ work on scientific and engineering problems. There are a number of tools that are useful for EDA, but EDA is characterized more by the attitude than by particular techniques. Typical graphical techniques used in EDA are: Box plot, Histogram, Multi-vari chart, Run chart, Pareto chart, Scatter plot, Stem-and-leaf plot, Parallel coordinates, etc.