F-test

The F-test is a type of significance test which compares the standard deviations of two samples in order to determine if the standard deviations of their parent populations are equal.

We give the standard deviations of the populations the symbols σ1 and σ2.

We give the standard deviations of the samples the symbols s1 and s2.

The hypothesis in an F-test are:
H0: σ1 = σ2
Ha: σ1 ≠ σ2

The test statistic (F) is given by:

The F-statistic has a unique distribution with two separate parameters for its degrees of freedom. This is because both of the sample standard deviations has have their own degrees of freedom value.

The first parameter is the degrees of freedom in the denominator(df1), which = (the number of observations in the sample with the larger s) – 1.
The second parameter is the degrees of freedom in the numerator(df2), which = (the number of observations in the sample with the smaller s) – 1.

To find at what level your data is significant you can look at a table of F distribution critical values. (some here) Find the highest significance level(α) for which your F-statistic is larger than the F critical value, then double that α (because Ha is two-sided.)
This is super-inconvenient though, so if possible use software to find the P-value exactly.