Answer: F-test.

## Explanation The F-test is the most appropriate statistical test for comparing the variances of two normally distributed populations. Here's why: ### Key Points: 1. **F-test Purpose**: The F-test is specifically designed to compare the variances of two independent samples from normally distributed populations. 2. **Test Statistic**: The F-statistic is calculated as the ratio of the two sample variances: $$F = \frac{s_1^2}{s_2^2}$$ where $s_1^2$ and $s_2^2$ are the sample variances. 3. **Assumptions**: The F-test assumes that both populations are normally distributed and that the samples are independent. ### Why Other Options Are Incorrect: - **B. Chi-square test**: This test is used for testing variance of a single population against a hypothesized value, not for comparing variances of two populations. - **C. Paired comparisons test**: This test (like paired t-test) is used for comparing means of two related samples, not variances. ### Application in CFA Context: In financial analysis, the F-test is commonly used to: - Test for equal variances in regression analysis (homoscedasticity) - Compare risk measures between two investment portfolios - Analyze differences in volatility between two asset classes ### Important Note:** When using the F-test, it's conventional to place the larger sample variance in the numerator to ensure the F-statistic is ≥ 1. This simplifies the interpretation of the test results.

Author: LeetQuiz .