What does Pearson's correlation measure?

Pearson's correlation specifically measures the strength and direction of a linear relationship between two continuous variables. It provides a numerical value ranging from -1 to 1, where a value of 1 indicates a perfect positive linear relationship, a value of -1 indicates a perfect negative linear relationship, and a value of 0 suggests no linear relationship at all. This makes it a valuable tool for understanding how changes in one variable might relate to changes in another, particularly when assessing trends in data.

Other options such as causation, variability, and types of distributions do not accurately capture what Pearson's correlation is designed to measure. While it is possible for correlated variables to indicate a relationship, correlation itself does not imply that one variable causes changes in the other. Variability refers to the spread of data and is not the focus of Pearson's correlation. Similarly, examining types of data distributions falls outside the scope of what this correlation type analyzes. Thus, the correct response highlights the fundamental purpose of Pearson's correlation in statistical analysis.