Which mathematical technique is used to explore the relationship between two variables?

The technique that is commonly used to explore the relationship between two variables is correlation. Correlation measures the strength and direction of a linear relationship between two variables. It provides a statistical figure, usually between -1 and +1, which indicates how closely the two variables move together. A correlation coefficient close to +1 implies a strong positive relationship, whereas a value close to -1 indicates a strong negative relationship. Values around 0 suggest little to no linear relationship.

This method is particularly useful in psychology and other social sciences, as it can indicate whether changes in one variable might be associated with changes in another. However, correlation does not imply causation; it only reflects the degree of association between the variables analyzed.

Other options, while related to data analysis and statistics, serve different purposes. For example, regression is more focused on predicting the value of a dependent variable based on one or more independent variables. Variance measures how much data points differ from the mean of a dataset, giving insight into data distribution rather than relationships. Abstraction refers to the process of simplifying complex reality by modeling it. Thus, correlation is specifically tailored for analyzing the relationship between two variables, making it the most appropriate choice in this context.