What does Pearson's r correlation coefficient measure?

Pearson's r correlation coefficient specifically measures the strength and direction of the linear relationship between two continuous variables. When calculating this coefficient, you obtain a value that ranges from -1 to 1. A value of 1 indicates a perfect positive linear correlation, where increases in one variable result in proportional increases in the other. Similarly, a value of -1 indicates a perfect negative linear correlation, where increases in one variable result in proportional decreases in the other. A value of 0 suggests no linear correlation between the two variables. This coefficient is widely used in statistics to evaluate how closely associated the two sets of data are, and whether a linear relationship is evident.

The other options do not accurately represent what Pearson's r is designed to measure. The average of a dataset relates to central tendency, while variability pertains to how much data points differ from the mean. Bias concerns systematic errors in measurements rather than relationships between data sets.