Percentile Calculator

Find the value at any percentile in a dataset using linear interpolation, with median and summary statistics.

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Dataset (numbers) Percentile (0–100)

P25 value

Median (P50)

Count

P25 value	20
Median (P50)	35
Minimum	15
Maximum	50
Count	5

Descriptive Statistics

Percentile calculator — find any percentile in your dataset

A percentile tells you what percentage of values in a dataset fall below a given point. The 25th percentile (Q1) means 25% of values are lower; the 90th percentile means 90% of values are lower. Percentiles are used everywhere from standardised test scoring and growth charts to salary benchmarking and performance analytics.

About this calculator

Reviewed 2026-03-15

Methodology

Values are parsed and sorted numerically in ascending order. The fractional index L = (P/100) × (n−1) is computed. If L is an integer, the result is sorted[L]. Otherwise, linear interpolation is applied between sorted[floor(L)] and sorted[ceil(L)]. This is equivalent to Excel PERCENTILE.INC and NumPy percentile with interpolation="linear".

Limitations

Results may differ slightly from calculators using the exclusive method (Excel PERCENTILE.EXC) or the nearest-rank method.
For very small datasets (n < 5), percentile estimates are unreliable as there are too few data points to interpolate meaningfully.
Percentile calculations assume all entered values are from the same population. Mixing units or populations will produce meaningless results.

Disclaimer

This calculator is for educational and analytical use. Percentile results depend on the interpolation method chosen.

Sources

See our calculation methodology and editorial standards for how this estimate is produced.

How percentiles are calculated

This calculator uses linear interpolation — the same method as Excel's PERCENTILE.INC function and NumPy's default. For percentile P and a sorted dataset of n values, the index is L = (P/100) × (n − 1). If L is a whole number, the result is the value at that index. If L falls between two indices, the result is interpolated: value[floor(L)] + fractional_part × (value[ceil(L)] − value[floor(L)]).

For example, in the dataset [15, 20, 35, 40, 50] with n=5, the 25th percentile index is 0.25 × 4 = 1. The value at index 1 is 20, so P25 = 20. For the 30th percentile: index = 0.30 × 4 = 1.2, so P30 = 20 + 0.2 × (35 − 20) = 23.

Common percentiles

The most frequently used percentiles are the quartiles: P25 (Q1), P50 (median, Q2), and P75 (Q3). The 90th and 95th percentiles are common in performance benchmarking. The 5th and 95th percentiles together define the 90% range. The 99th percentile is used in latency measurements and outlier analysis.

In standardised testing, scores are often reported as percentile ranks. A percentile rank of 85 means the student scored higher than 85% of test takers — not that they got 85% of questions right.

Percentile vs. percentile rank

A percentile value is a number from the dataset (e.g. the 90th percentile score is 720). A percentile rank is a position (e.g. your score of 720 is at the 90th percentile rank). This calculator finds the value at a given percentile position.

Note that the 50th percentile equals the median, and the 0th and 100th percentiles equal the minimum and maximum values respectively.

Frequently asked questions

What is the difference between percentile and percentage?

A percentage is a ratio expressed out of 100 (e.g. 75%). A percentile is a position in a ranked dataset. Scoring 75% on a test means you got 75 of 100 questions right. Being at the 75th percentile means you scored higher than 75% of all test takers — you could achieve this by scoring 60% if most others scored lower.

Why does this differ from other percentile calculators?

There are several methods for computing percentiles (exclusive, inclusive, nearest-rank, etc.). This calculator uses the linear interpolation / inclusive method (Excel PERCENTILE.INC, Python numpy default). Other methods may produce slightly different results, especially for small datasets.

What is the percentile in a normal distribution?

For a normal distribution, percentiles correspond to z-scores. The 50th percentile is the mean (z=0), the 84th percentile is one standard deviation above the mean (z=1), and the 97.7th percentile is two standard deviations above (z=2). Use the normal distribution calculator to find exact percentiles for a given mean and standard deviation.

Percentile Calculator

Percentile calculator — find any percentile in your dataset

How percentiles are calculated

Common percentiles

Percentile vs. percentile rank

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