What does correlation measure in statistics?

Correlation measures the strength and direction of a relationship between two variables. When analyzing data, correlation helps to determine how closely related these variables are to each other. A strong positive correlation indicates that as one variable increases, the other variable tends to increase as well, while a strong negative correlation indicates that as one variable increases, the other tends to decrease. This relationship is quantified using a correlation coefficient, which ranges from -1 to 1.

In contrast, other options focus on different statistical concepts. For instance, the average of a data set refers to the mean, which provides a central value but does not convey any information about relationships between variables. The difference between two means would apply to hypothesis testing or comparing groups rather than measuring relationships, and frequency of data occurrence pertains to how often data points appear in a dataset, which is unrelated to understanding the association between two variables. Therefore, the first choice accurately captures the essence of what correlation reveals in statistical analysis.