Outliers are pieces of data within a set that are noticeably different from the rest of the data set. Oftentimes this means that a particular data point is far larger, or far smaller, than the other data points. Outliers may emerge for several reasons, such as an error in taking or recording data. Regardless of the reason, it is important to identify what data points are outliers and to determine why they are so, since the existence of outliers may indicate either a flaw in the testing methodology or perhaps hint at factors not being considered. One such method for identifying outliers is by finding upper and lower fences, that bound the majority of the data set. Any data outside of these fences could be considered outliers. The following equations indicate how to find such fences.