Enter your data set (comma-separated values) and a specific value to find its percentile rank — the percentage of values in the dataset that fall at or below your chosen value. You'll see the percentile rank (%), the count of values below, and the total number of values used in the calculation.
Percentile Rank
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Number of Values Below
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Number of Values Equal
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Total Values (N)
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Interpretation
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A percentile rank is a statistical measure that indicates the percentage of values in a dataset that fall at or below a specific value. For example, a percentile rank of 75 means the value is higher than 75% of the data points in the set. It's commonly used in educational testing, health assessments, and performance analysis.
The standard formula is: PR = (L / N) × 100, where L is the number of values in the dataset that are less than the target value, and N is the total number of values. Some formulas also account for values equal to the target by using (L + 0.5E) / N × 100, where E is the count of values equal to the target.
A percentile is a value below which a given percentage of data falls — for example, the 90th percentile is the value at which 90% of the data lies below. A percentile rank, on the other hand, tells you what percentage of data falls at or below a specific given value. They are inverse operations of each other.
The maximum percentile rank is 100, which would mean a value is higher than all other values in the dataset. In practice, using the standard formula (L/N × 100), the highest possible result is just below 100 unless a value is strictly greater than every other value in the set.
A 95th percentile rank means that the value in question is higher than 95% of all the values in the dataset. Only 5% of values are above it. This is often considered an excellent result in contexts like standardized testing or clinical growth charts.
In simple terms, your percentile rank tells you how you compare to everyone else in a group. If your score is at the 80th percentile, you scored higher than 80 out of every 100 people in that group. It's a quick way to understand relative standing within a dataset.
Yes. If your target value is lower than all values in the dataset, its percentile rank will be 0. If it's higher than all values, the rank will approach 100. The formula still applies — L (values below the target) would be 0 or N respectively, giving you the boundary percentile ranks.
Percentile rank is widely used in education (standardized test scores like SAT, GRE), healthcare (growth charts for children's height and weight), business analytics (sales performance benchmarking), and psychology (norm-referenced assessment scoring). It provides a meaningful relative comparison across a distribution.