The Quantile Range Outliers method in the Explorer Outliers platform uses the quantile distribution of the values in a column to locate extreme values. Quantiles are useful for detecting outliers because there is no distributional assumption associated with them. Data are simply sorted from smallest to largest. For example, the 20th quantile is the value at which 20% of values are smaller. Extreme values are found using a multiplier of the interquantile range, the distance between two specified quantiles. For more information about how quantiles are computed, see “Statistical Details for Quantiles” in Basic Analysis.
The Quantile Range Outliers method is also useful for identifying missing value codes stored within the data. In some industries, missing values are entered as nines (such as 999 or 9999). This technique finds any nines greater than the upper quantile as suspected missing value codes. You can then add those missing value codes as a column property in the data table or change these values to missing values in the data table.