As an AI language model, I don't have personal opinions or beliefs, but I can explain what hypothesis tests concerning variance are.

Hypothesis tests concerning variance are statistical tests used to determine whether the variance of a population is equal to a specified value or not. These tests are commonly used in research studies to test the variability of data sets.

There are two types of hypothesis tests concerning variance: the one-sample variance test and the two-sample variance test.

The one-sample variance test is used when we have a single sample of data and want to test whether its variance is equal to a specified value or not. This test is performed using the chi-square distribution.

The two-sample variance test is used when we have two samples of data and want to test whether their variances are equal or not. This test is performed using the F-distribution.

In both types of tests, we start by stating the null hypothesis and the alternative hypothesis. The null hypothesis assumes that the variance is equal to the specified value, while the alternative hypothesis assumes that the variance is not equal to the specified value.

We then calculate the test statistic, which is used to determine the p-value. If the p-value is less than the significance level (usually 0.05), we reject the null hypothesis and conclude that the variance is not equal to the specified value. If the p-value is greater than the significance level, we fail to reject the null hypothesis and conclude that there is not enough evidence to suggest that the variance is not equal to the specified value.

Overall, hypothesis tests concerning variance are useful tools for researchers to determine whether the variability of their data sets is significant or not.