The sum of squares and degree of freedom calculation from the previous videos are put into a ratio to calculate the F Value, on whose basis the null hypothesis is confirmed or rejected. If variance is higher between samples than within the null hypothesis is more likely to be rejected. The results of a numerical example are interpreted more abstractly and then tested with regards to a confidence interval and the corresponding F table.

The definition of a chi-square distribution is given. Chi-square is defined as the sum of random normally distributed variables (mean=0, variance=s.d.=1). The number of added squared variables is equal to the degrees of freedom. With more degrees of freedom the probability of larger chi-square values is increased.

The chi-square distribution is used to test a hypothesis. Therefore, expected values are related to observed values using a chi-square distribution. Then using p-value tables the hypothesis is tested at a 5% significance level.