Correlation and linear regression are common methods to measure the relationship between two numbers. Correlation shows how strong the connection is, while linear regression predicts one number based on another. Pearson's correlation is used when both numbers are normally distributed, and Spearman's rho is used when they're not. A hypothesis test checks if the relationship is real, and a confidence interval gives an idea of how strong it is. The coefficient of determination tells us how much of one number's variability is due to the other. It's important to check for a linear relationship with a scatter plot before using these methods, as outliers can skew results. Remember, correlation doesn't always mean causation.