What quartile method does this calculator use?

The exclusive method (also called Tukey's method): the median is excluded when computing Q1 and Q3 from the lower and upper halves. This matches most statistics textbooks and R's default quartile type 6 is similar.

What is the 1.5×IQR rule?

Any value below Q1 - 1.5×IQR or above Q3 + 1.5×IQR is considered a mild outlier. Values beyond 3×IQR are considered extreme outliers. This rule was introduced by John Tukey in 1977.

Why is IQR better than standard deviation for outlier detection?

Standard deviation is influenced by extreme values, which can mask outliers. IQR depends only on the middle 50% of data and is therefore resistant (robust) to the very outliers you are trying to find.

Can I use this for an even or odd number of values?

Yes. For an odd count the median is the exact middle value; for an even count it is the average of the two middle values. Q1 and Q3 are computed from each half excluding the median.

What does an IQR of zero mean?

An IQR of zero means at least the middle 50% of your values are identical. This is common in highly discrete datasets. Outlier detection still works — values beyond the fence (if any) are still flagged.

Interquartile Range Calculator

Name: Interquartile Range Calculator
Availability: InStock
Author: Nham Vu

Enter comma-separated numbers to instantly compute Q1, Q2, Q3, IQR, and detect outliers via the 1.5×IQR rule.

Dataset Input

Numbers (comma or space separated)

Quick Examples

Enter numbers above and click Calculate IQR

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Summary

Enter comma-separated numbers to instantly compute Q1, Q2, Q3, IQR, and detect outliers via the 1.5×IQR rule.

How it works

Enter your numbers separated by commas (or spaces) in the input box.
The calculator sorts the dataset and computes Q1, Q2 (median), and Q3 using the exclusive-quartile method.
IQR is calculated as Q3 - Q1.
Lower fence = Q1 - 1.5×IQR; Upper fence = Q3 + 1.5×IQR.
Any value outside these fences is flagged as an outlier.
Results update instantly as you type.

Use cases

Verify homework or exam solutions for descriptive statistics.
Identify outliers in survey, lab, or sensor data before modeling.
Determine box-plot whisker boundaries for data visualization.
Assess data spread without sensitivity to extreme values.
Quality control: flag measurements that fall outside expected ranges.
Compare dataset variability across different groups or time periods.

Frequently Asked Questions

Last updated: 2026-06-10 · Reviewed by Nham Vu