The bad news is that the F test is strongly affected and often rendered invalid by violation of the normality assumption. A lack of independence within a sample is often caused by the existence of an implicit factor in the data. If the data are in a particular order, consider the possibility of dependence. If you are unsure whether your samples are independent, you wish may to consult a statistician or someone who is knowledgeable about the data collection scheme you are using. Values may not be identically distributed because of the presence of outliers .
Outliers are anomalous values in the data.
Outliers tend to increase the estimate of a sample variance. In particular, one or more outliers in a single sample will tend to make the F statistic too large, thus increasing the chance of incorrectly concluding that the population variances differ. Outliers may be due to recording errors, which may be correctable, or they may be due to the sample not being entirely from the same population. Apparent outliers may also be due to the values being from the same, but nonnormal , population. The F statistic is based on the the sample variances, both of which are sensitive to outliers .