What Makes the Student-t Distribution Ideal for Outlier Analysis

Outliers can mess up your data by changing key numbers. Extreme values can change the average, making results wrong. For example, one outlier can raise the average too much. This can lead to false ideas about the data. Finding these odd values is important to keep results correct. The student-t distribution is a good way to do this. It works well with d…