What is correlation analysis used for?

Correlation analysis is specifically designed to measure the strength and direction of relationships between two or more variables. It assesses whether and how strongly pairs of variables are related, which can help researchers understand how changes in one variable may be associated with changes in another.

For example, if researchers want to explore the relationship between advertising spend and sales revenue, correlation analysis can reveal whether increases in advertising are statistically related to increases (or decreases) in sales. The correlation coefficient quantifies this relationship on a scale from -1 to 1, where values close to 1 indicate a strong positive relationship, values close to -1 indicate a strong negative relationship, and values around 0 suggest no relationship at all.

This focus on measuring relationships is distinct from other options: quantifying the likelihood of an outcome relates more to probability and forecasting, establishing cause-and-effect relationships involves more rigorous methodologies such as experiments, and summarizing data characteristics pertains to descriptive statistics rather than relational analysis.