Answer: Chi-squared test

The chi-squared test is the most common hypothesis test for the variance of a normally distributed population. This is because when testing the variance of a normal distribution, the test statistic follows a chi-squared distribution. The chi-squared test statistic is calculated as: $$\chi^2 = \frac{(n-1)s^2}{\sigma_0^2}$$ where: - $n$ is the sample size - $s^2$ is the sample variance - $\sigma_0^2$ is the hypothesized population variance This test statistic follows a chi-squared distribution with $(n-1)$ degrees of freedom under the null hypothesis. **Why other options are incorrect:** - **F-test**: Used for comparing variances of two normally distributed populations, not testing a single variance against a hypothesized value. - **Z-test**: Used for testing means when population variance is known, not for testing variances. - **t-test**: Used for testing means when population variance is unknown, not for testing variances.

Author: Nikitesh Somanthe