> Exploratory Data Analysis (EDA) is an analysis approach that identifies general patterns in the data. These patterns include outliers and features of the data that might be unexpected. > EDA is an important first step in any data analysis. Understanding where outliers occur and how variables are related can help one design statistical analyses that yield meaningful results. In biological monitoring data, sites are likely to be affected by multiple stressors. Thus, initial explorations of stressor correlations are critical before one attempts to relate stressor variables to biological response variables.  EDA can provide insights into candidate causes that should included in a causal assessment.[Exploratory Data Analysis | US EPA](https://www.epa.gov/caddis-vol4/exploratory-data-analysis) > Exploratory Data Analysis (EDA) is an analysis approach that identifies general patterns in the data. These patterns include outliers and features of the data that might be unexpected. > EDA is an important first step in any data analysis. Understanding where outliers occur and how variables are related can help one design statistical analyses that yield meaningful results. In biological monitoring data, sites are likely to be affected by multiple stressors. Thus, initial explorations of stressor correlations are critical before one attempts to relate stressor variables to biological response variables.  EDA can provide insights into candidate causes that should included in a causal assessment. Source(s): [Exploratory Data Analysis | US EPA](https://www.epa.gov/caddis-vol4/exploratory-data-analysis) *** 2024 cloudedknowledge.net