Exploratory Data Analysis (EDA) is a crucial phase in data analysis that involves summarizing and visualizing data to uncover patterns, anomalies, and relationships. One key aspect of EDA is examining correlation and causation. Correlation measures the strength and direction of a relationship between two variables, often quantified using correlation coefficients. For example, a high positive correlation indicates that as one variable increases, the other tends to increase as well. However, correlation alone does not imply causation. Causation implies that one variable directly influences another. Distinguishing between correlation and causation is essential because two variables may be correlated due to a third factor or by mere coincidence. EDA helps identify these relationships but does not establish causation; additional statistical methods and experimental designs are needed to draw causal inferences.