Correlation – Pearson and Spearman - One Line Questions

1. A perfect positive rank correlation using Spearman's rho would result in a value of: — 1
2. If the ranks of two variables are identical for all data points, what will be the value of Spearman's rho? — 1
3. If the ranks of two variables are perfectly reversed (highest rank for one corresponds to lowest for the other), Spearman's rho will be: — -1
4. What is the range of possible values for a Pearson correlation coefficient (r)? — -1 to 1
5. Which of the following correlation coefficients indicates the strongest linear relationship? — -0.9
6. If the Pearson correlation coefficient is 0.8, the coefficient of determination (r-squared) is: — 0.64
7. When calculating Pearson's r, if one of the variables has zero variance (all values are the same), the correlation coefficient will be: — Undefined
8. What does a Pearson correlation coefficient of 0 indicate? — No linear relationship between the variables.
9. A scatterplot showing a clear upward trend from left to right, but with a noticeable curve, might suggest: — A weak Pearson correlation but potentially a strong Spearman correlation.
10. A correlation coefficient of 0.05 between hours of sleep and exam performance would indicate: — A very weak positive linear relationship.
11. A correlation coefficient of -0.95 suggests: — A strong negative linear relationship.
12. What does a Spearman's rho of -0.75 suggest? — A strong negative monotonic relationship between the ranks of the variables.
13. If Pearson's r is calculated on data that is not normally distributed, the p-value may be: — Unreliable
14. Which of the following statements about correlation is TRUE? — Both B and C are true.
15. A researcher finds a strong positive correlation between ice cream sales and drowning incidents. What is the most likely interpretation? — There is a confounding variable (e.g., hot weather) influencing both.
16. Which of the following is a graphical method to visualize the relationship between two variables? — Scatterplot
17. A monotonic relationship means that as one variable increases, the other variable: — Either consistently increases or consistently decreases, but not necessarily at a constant rate.
18. What is a key advantage of Spearman's correlation over Pearson's correlation? — It does not require the data to be normally distributed.
19. Spearman's rho is less affected by outliers than Pearson's r because: — It transforms the data into ranks, which compresses the range of values.
20. Spearman's rho is a measure of: — Monotonic association.
21. Which scenario would be most appropriate for using Spearman's rank correlation? — Measuring the relationship between a doctor's rating of pain severity (ordinal) and a patient's reported pain level (ordinal).
22. The formula for Spearman's rho involves calculating the difference between the ranks of paired observations and then: — Summing the squares of these differences and applying a specific formula.
23. Which type of correlation indicates that as one variable increases, the other variable also increases? — Positive correlation
24. Which type of variable is most suitable for Pearson correlation? — Interval/Ratio
25. Pearson's r is sensitive to which type of data? — Interval or Ratio data
26. Pearson's correlation coefficient is most appropriate when the relationship between variables is: — Linear and interval/ratio.
27. If two variables have a strong positive Pearson correlation, what can be inferred? — As one variable increases, the other tends to increase linearly.
28. Which of the following is NOT a type of correlation coefficient? — Student's t
29. Which correlation coefficient is generally considered more robust to non-normality and outliers? — Spearman's rho
30. Spearman's rank correlation coefficient is denoted by: — rho (ρ)
31. Pearson's correlation coefficient is also known as: — Product-moment correlation coefficient
32. What is the term for a correlation that appears to exist between two variables but is actually due to the influence of a third, unmeasured variable? — Spurious correlation
33. Which statistical test is used to calculate Pearson's correlation coefficient? — None of the above (it's a formula)
34. Spearman's correlation coefficient measures the strength and direction of the relationship between: — The ranks of two variables.
35. What does correlation analysis aim to measure? — The strength and direction of the linear relationship between two variables.
36. What is a common assumption for using Pearson's correlation coefficient? — The variables must be normally distributed.
37. Spearman's rho is a non-parametric measure, meaning it does not rely on assumptions about: — The distribution of the data (e.g., normality).
38. A correlation matrix displays: — The pairwise correlation coefficients between multiple variables.
39. Spearman's correlation works by calculating the Pearson correlation coefficient on: — The ranks of the data for each variable.
40. The primary difference between Pearson's r and Spearman's rho lies in: — The type of relationship they measure (linear vs. monotonic).
41. What does the coefficient of determination (r-squared) represent? — The proportion of variance in one variable that is predictable from the other variable.
42. The formula for Pearson's r involves: — The product of deviations from the mean for each variable.
43. Pearson's correlation coefficient is derived from: — The covariance of the two variables divided by the product of their standard deviations.
44. The null hypothesis for a correlation test typically states: — There is no significant correlation between the variables.
45. If a dataset has tied ranks, how is Spearman's rho typically calculated? — The average rank is assigned to tied values.
46. What is the primary purpose of hypothesis testing in correlation analysis? — To determine if the observed correlation in the sample is likely to exist in the population.
47. Spearman's correlation is used when: — Data is ordinal or when the assumption of normality for Pearson's r is violated.
48. When is it generally advisable to use Spearman's rho instead of Pearson's r? — When there are outliers that might unduly influence Pearson's r, or when data is ordinal.
49. When would you choose Kendall's tau correlation over Spearman's rho? — When dealing with very large datasets and many tied ranks.
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