What does correlation in statistics measure?

Correlation in statistics specifically measures the strength and direction of the relationship between two variables. When we talk about correlation, we are referring to how one variable may change in relation to another. A positive correlation indicates that as one variable increases, the other also tends to increase, while a negative correlation suggests that as one variable increases, the other tends to decrease.

Understanding correlation is key in data analysis, as it helps identify potential relationships that could be important for predictive analytics or hypothesis testing. For instance, knowing that there is a correlation between two variables can guide further investigation or inform decisions based on the nature of their relationship, which is critical in research and data-informed decision-making.

The other options, while relevant to statistical concepts, do not define correlation. They refer to different statistical measures: the average pertains to measures of central tendency, central tendency itself is a concept that describes the center of a data set, and variance specifically measures the dispersion within a data set, rather than any relationship between variables.