If the data are bimodal instead of unimodal, for example, indications of skewness , heavy-tailedness , or outliers may be due to the bimodality. Although the letter value display is designed to be robust and to capture most of the information about distributional shape, it is not the most powerful test for nonnormality available. And with small sample size the letter value display will not be able to give much information about the distributional shape. 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.
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 letter values display itself may also be able to indicate the presence of outliers. A boxplot or normal probability plot of the sample values can reveal lack of symmetry , nonnormal tails , and suspected outliers. A histogram of the sample values can reveal possible bimodality in the data, or, with large sample sizes, nonnormal shape .