What does correlation measure in statistics?

Correlation measures the extent to which two or more variables fluctuate together, indicating how changes in one variable are associated with changes in another. This relationship is quantified by a correlation coefficient, which can range from -1 to +1. A positive correlation indicates that as one variable increases, the other variable tends to increase as well. Conversely, a negative correlation suggests that as one variable increases, the other tends to decrease. Correlation does not imply causation; it simply identifies patterns of association between variables, which can be extremely valuable in fields like finance, economics, and social sciences for understanding relationships and making predictions.

The other options do not pertain to the concept of correlation. The average of a data set refers to the central tendency, which is distinct from measuring relationships between variables. The specific value of a variable indicates a single point in a data set and not how multiple variables interact with each other. The frequency distribution of data points describes how often each value occurs, which is unrelated to the concept of correlated fluctuations between variables.