Outlier Calculator

An outlier is a data value that sits far away from the bulk of the other values in a data set. Outliers can arise from genuine extreme observations (a very tall person in a height survey), from measurement error, or from data entry mistakes. Identifying them matters because a single outlier can dramatically shift summary statistics like the mean and standard deviation, making your analysis misleading. The IQR method, also called the Tukey fences method, is one of the most widely taught and robust approaches to outlier detection. It works by computing the interquartile range (IQR = Q3 minus Q1, the spread of the middle 50% of the data) and then setting two boundaries called fences. The lower fence is Q1 minus 1.5 times the IQR. The upper fence is Q3 plus 1.5 times the IQR. Any value that falls outside these fences is classified as a potential outlier. Because the fences are built from the middle of the distribution rather than from the extremes, they are not themselves distorted by the very outliers they are trying to detect. That makes the IQR method more appropriate than standard deviation methods for skewed distributions. This calculator accepts a comma-separated list of numbers, computes Q1, Q3, and IQR using linear interpolation, then applies the 1.5 x IQR rule. It returns the lower and upper fences, a list of any outlying values found, the total data count, and the count of clean (non-outlier) values. It is useful for students, data analysts, teachers, and anyone cleaning a data set before further statistical analysis.

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