Dangers of overinterpretation

Features in the distribution of a small data set may not be meaningful.

Be careful not to overinterpret patterns in small data sets. Clusters, outliers or skewness may appear by chance even if there is no meaningful basis to these features.

Pronounced outliers or clusters may be taken as indicative of something meaningful in the underlying process. However less pronounced outliers or clusters must be supported by outside evidence before these features can be interpreted as meaningful.