Exploratory Data Analysis (EDA) is a method of analyzing data sets to summarize their main characteristics, often with the use of visual methods. This can include looking at the distribution of variables, identifying any outliers or anomalies, and testing for relationships between variables. The goal of EDA is to provide a better understanding of the data and to identify any potential issues or patterns that might not be immediately apparent.

Exploratory Data Analysis (EDA) is best used when the goal is to gain a better understanding of a data set, without making any specific assumptions or testing any specific hypotheses. EDA is a useful starting point for any data analysis project, as it can help identify any potential issues or patterns in the data that might not be immediately apparent.

Statistical Data Analysis, on the other hand, involves using formal statistical methods to analyze data sets. This can include testing specific hypotheses about the data, estimating the strength of relationships between variables, and making predictions about future outcomes. The goal of statistical data analysis is to draw conclusions and make inferences about a population based on the information in a sample. Unlike EDA, statistical data analysis typically involves making assumptions about the data and using formal statistical tests to evaluate them.

Statistical Data Analysis, is best used when the goal is to test specific hypotheses about the data or to make predictions about future outcomes. Statistical methods are well-suited for making inferences about a population based on the information in a sample, and can help to identify any statistically significant relationships or trends in the data.

In general, EDA and statistical data analysis are complementary approaches that can be used together to gain a better understanding of a data set. EDA can provide a broad overview of the data, while statistical analysis can help to test specific hypotheses and draw more precise conclusions.