For example, if subjects receive more than one treatment, then there may be carryover effects from one treatment to the next. If the data are in a particular order, consider the possibility of dependence. In fact, since the subjects are random, then there generally is a correlation for measurements within a subject, although it is assumed to be constant. If the serial correlation is positive, then the actual P value is much greater than the reported P value, making it possible to falsely conclude that there are significant differences when none exist. Examining the residuals , from which the linear effects of both the treatment factor and the subject factor have been removed, may be an easier way to uncover an implicit factor.
Outliers are anomalous values in the data.
They 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 factor level means and the sample variances, each of which is sensitive to outliers . In particular, a large outlier can inflate the overall variance, decreasing the F statistic and thus perhaps eliminating a significant difference. A nonparametric test may be a more powerful test in such a situation.