Covariance and correlation are both statistical measures that help to understand the relationship between two continuous variables. Covariance indicates the direction of the relationship between the variables, showing whether they tend to increase or decrease together. A positive covariance means that as one variable increases, the other also tends to increase, while a negative covariance indicates that as one variable increases, the other tends to decrease. However, the value of covariance can vary widely, making it difficult to interpret ¹².

Correlation, on the other hand, is a standardized measure of the relationship between two variables. It provides both the direction and strength of the relationship, with values ranging from -1 to 1. A correlation of 1 indicates a perfect positive relationship, -1 indicates a perfect negative relationship, and 0 indicates no relationship. Correlation is dimensionless, meaning it is not affected by the scale of the variables, making it easier to interpret compared to covariance ²³.

In summary, while both measures assess relationships between variables, correlation is often preferred due to its interpretability and standardization.