If the data are bimodal instead of unimodal, for example, indications of skewness , heavy-tailedness , or outliers may be due to the bimodality. The frequency table and histogram are designed to summarize the distribution of large samples. With a small sample size the frequency table will not be able to give much information about the distributional shape, and the histogram and other graphs may be uninformative. 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 histogram may also be able to detect outliers when the sample sizes are fairly large or the outliers are fairly extreme. A boxplot or normal probability plot of the sample values can also 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 .