Which test is the parametric method for assessing the relationship between two continuous variables (correlation)?

Which test is the parametric method for assessing the relationship between two continuous variables (correlation)?

• A. Pearson
• B. Spearman
• C. Kendall tau
• D. Chi-square

Answer: (A)

To gauge a linear relationship between two continuous variables using a parametric approach, you use Pearson correlation. It specifically measures how well the data fit a straight-line relationship and assumes the variables are roughly normally distributed, the relationship is linear, and the spread of one variable is similar across the range of the other (homoscedasticity). The result is a correlation coefficient that ranges from -1 to 1, with values near ±1 indicating a strong linear association and values near 0 indicating little to no linear relationship; significance is assessed with a p-value through a test of no linear relationship. This is the parametric option because it relies on the actual data values and their distributional properties. Nonparametric alternatives like Spearman rank correlation and Kendall tau use ranks and detect monotonic relationships without assuming normality or linearity, making them robust to outliers or non-normal data. Chi-square, in contrast, is used to test association between categorical variables, not the correlation between continuous ones.

To gauge a linear relationship between two continuous variables using a parametric approach, you use Pearson correlation. It specifically measures how well the data fit a straight-line relationship and assumes the variables are roughly normally distributed, the relationship is linear, and the spread of one variable is similar across the range of the other (homoscedasticity). The result is a correlation coefficient that ranges from -1 to 1, with values near ±1 indicating a strong linear association and values near 0 indicating little to no linear relationship; significance is assessed with a p-value through a test of no linear relationship. This is the parametric option because it relies on the actual data values and their distributional properties. Nonparametric alternatives like Spearman rank correlation and Kendall tau use ranks and detect monotonic relationships without assuming normality or linearity, making them robust to outliers or non-normal data. Chi-square, in contrast, is used to test association between categorical variables, not the correlation between continuous ones.