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Percentile Calculator

Find the percentile of a dataset.

Result

26.50

How it is calculated

The values are sorted from smallest to largest, then the percentile is found by linear interpolation: rank = p / 100 × (n − 1), counted from zero, where n is the number of values and p is between 0 and 100. If the rank falls between two positions, the result is xlo + (rank − lo) × (xhi − xlo). Numbers can be separated by commas, spaces or line breaks; anything that is not a number is silently skipped. The result is rounded to two decimals. Other software may use a different convention, so results can differ slightly from yours elsewhere.

Worked example

The data are 12, 15, 11, 20, 18, 25, 14, 30 and p = 90. Sorted: 11, 12, 14, 15, 18, 20, 25, 30, so n = 8. Rank = 0.9 × 7 = 6.3, which lies between position 6 (value 25) and position 7 (value 30). Result = 25 + 0.3 × (30 − 25) = 26.5. The 90th percentile is 26.50.

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Common mistakes

  • Typing the percentile as a fraction (0.9) instead of 90; 0.9 gives almost the minimum.
  • Expecting the result to be one of the data values; with interpolation it often sits between two of them.
  • Mixing in labels or units such as 12kg; items that are not plain numbers are dropped, which changes n without warning.
  • Comparing with spreadsheet or statistics software that uses another percentile definition and getting a slightly different answer on small samples.
  • Confusing a percentile of the data with a percentile rank: this tool returns a value, not the position of a value.

About this calculator

This calculator finds the k-th percentile of a dataset — the value below which k% of the data points fall — using linear interpolation between the two closest ranked values when the exact percentile position doesn't land on a whole number, the same method used by Excel's PERCENTILE.INC function and most statistics software.

Teachers converting raw test scores into percentile rankings, HR analysts benchmarking a salary against a market range (the "75th percentile salary" for a role), and researchers summarizing a distribution of data (reporting the 25th, 50th and 75th percentiles as a box-plot summary) all use this calculation to describe where a value sits relative to the rest of a dataset.

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