Statistics
Find percentile values, quartiles, and IQR from a data set
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%
?th Percentile
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Q1 (25th Percentile)
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Q2 / Median (50th)
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Q3 (75th Percentile)
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IQR (Q3 - Q1)
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How it works
Locates a value within a distribution, either finding the value below which a given share of the data falls, or finding the rank of a value you already have. Percentiles need no distributional assumption, which makes them safe for skewed data.
Formula
Value at percentile P: index = (P / 100) × (N − 1), interpolating between neighbours Percentile rank of x = (count below x + 0.5 × count equal to x) / N × 100
Variables
Pthe target percentile, 0 to 100Nnumber of values in the sorted datasetindexzero-based position in the sorted list
Worked example
Inputs: Sorted scores 55, 61, 64, 68, 70, 72, 75, 79, 84, 91
- N = 10, so for the 90th percentile: index = 0.90 × 9 = 8.1
- Value at index 8 is 84, at index 9 is 91
- Interpolate: 84 + 0.1 × (91 − 84) = 84.7
- Rank of 75: 6 values below, 1 equal → (6 + 0.5) / 10 × 100
Result: 90th percentile ≈ 84.7; the score 75 sits at the 65th percentile
Notes
- At least nine different percentile definitions are in use. Linear interpolation (the default here, and in NumPy and Excel PERCENTILE.INC) can differ noticeably from the nearest-rank method on small datasets.
- The median is the 50th percentile, and quartiles are the 25th, 50th, and 75th. Interquartile range is Q3 less Q1.
- Latency work quotes p95 and p99 because averages hide tail behaviour. A service with a 40 ms mean can still have a 2-second p99 that most users notice.
- You cannot average percentiles across groups. Combining a p95 from two servers requires the raw data or a mergeable sketch, not the mean of the two figures.
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Frequently Asked Questions
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