How to calculate percentile rank
Percentile rank tells you what percentage of a data set falls at or below a given value: percentile rank = (count of values ≤ target ÷ n) × 100. For 10, 20, 30, 40, 50, 60, 70, 80 with a target of 50, five of the eight values (10, 20, 30, 40, 50) are at or below 50, so the rank is 5 ÷ 8 × 100 = 62.5%. Working in the opposite direction, from a rank to the value that sits there, is the job of the percentile calculator.
How to use this calculator
Enter your data in the value fields, leave unused fields blank, then enter the target value to rank. The result shows the percentile rank and how many values are at or below the target. For the quartile cut points instead, use the quartile calculator.
The data does not need to be sorted, and repeated values are counted individually, so ties push the rank upward.
Percentile rank vs. percentile
A percentile rank turns a value into a percentage position, while a percentile goes the other way — the value at a given percentage. A rank of 62.5% means the target sits above roughly 62.5% of the data. For a broader picture of the data's spread, see the five number summary calculator.
Frequently asked questions
- How do I calculate percentile rank?
- Divide the number of values at or below your target by the total count, then multiply by 100. For the example, 5 ÷ 8 × 100 = 62.5%.
- Does the target value count itself?
- Yes. This calculator counts values at or below the target (≤), so a value equal to the target is included.
- What is the difference between percentile and percentile rank?
- A percentile is the value at a given percentage; percentile rank is the percentage position of a given value.
- Can the percentile rank be 100%?
- Yes, if the target is the maximum value, since all values are at or below it.
- What if my target value is not in the data set?
- That works fine — the calculator simply counts how many values fall at or below it. A target of 45 in the example set still counts 4 of 8 values, giving a rank of 50%, which is useful for placing a new score against existing data.