Quartile Calculator
Split a data set into quartiles — minimum, Q1, median, Q3 and maximum — with the interquartile range, using linear interpolation.
How it works
Q1 = 25th, Q2 = 50th (median), Q3 = 75th percentile by linear interpolation · IQR = Q3 − Q1
Quartiles divide sorted data into four equal parts: Q1 marks the 25th percentile, Q2 the median at the 50th, and Q3 the 75th, with the minimum and maximum bracketing the ends. Together they form the five-number summary that a box plot draws, and the gap between Q1 and Q3 — the interquartile range — captures the spread of the middle half. This calculator locates the quartiles by linear interpolation on rank p·(n − 1), the inclusive method used by most spreadsheets.
Worked example
For 1, 2, 3, 4, 5, 6, 7, 8, 9 the minimum is 1 and the maximum is 9. The median Q2 is 5, and interpolation places Q1 at 3 and Q3 at 7, so the interquartile range is 7 − 3 = 4. A quarter of the data lies in each of the four intervals the quartiles create.
Sources: Quartile — Wikipedia
Frequently asked questions
What is the difference between quartiles and percentiles?
Quartiles are just three specific percentiles — the 25th, 50th and 75th — that carve the data into four equal groups. Percentiles are the general idea of splitting data into 100 parts, so every quartile is a percentile but only three percentiles are quartiles.
Why might my quartiles differ from another calculator?
Several accepted methods exist for locating quartiles. This tool uses linear interpolation on rank p·(n − 1) — matching Excel’s PERCENTILE.INC and NumPy — so 1…9 gives Q1 = 3 and Q3 = 7; Tukey’s exclusive median method can return slightly different Q1 and Q3.
What does the interquartile range tell me?
The IQR is the width of the middle 50% of the data, from Q1 to Q3. Because it discards the top and bottom quarters, it measures spread while ignoring extremes, which makes it a robust companion to the median and the basis of outlier fences.
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