Outlier Calculator
Find outliers in a data set using the 1.5×IQR rule, with the lower and upper fences shown.
A value is flagged as an outlier when it falls below Q1 − 1.5 × IQR or above Q3 + 1.5 × IQR.
How it works
Outliers are values that sit far from the rest of the pack. The most common way to catch them is the 1.5×IQR rule: anything below Q1 − 1.5×IQR or above Q3 + 1.5×IQR gets flagged as unusual.
This tool sorts your numbers, works out the first and third quartiles, and multiplies the interquartile range by 1.5 to set a lower fence and an upper fence. Any value outside that pair of fences is listed as an outlier.
It's the same logic a box plot uses to draw its whiskers and plot stray points. Spotting outliers is worth doing before you trust an average — a single typo or extreme case can quietly pull the mean off course.
Frequently asked questions
How does the 1.5×IQR rule work?
Take the interquartile range (Q3 − Q1), multiply it by 1.5, then flag any value below Q1 minus that amount or above Q3 plus it. Those cutoffs are called the lower and upper fences.
Why 1.5 and not some other number?
It's a convention from John Tukey that balances catching genuine outliers against flagging too many ordinary values. Using 3×IQR instead marks only extreme, 'far out' points.
Should I delete outliers I find?
Not automatically. An outlier can be a data-entry error worth fixing or a real, important observation. Investigate why it stands out before deciding to keep, correct, or set it aside.