The PERCENTILE function in Excel is a powerful tool used to calculate the k-th percentile of a specified dataset. This capability is particularly beneficial in statistical analysis, allowing users to understand the distribution of data by determining thresholds below which a percentage of observations fall. It is thus widely used for tasks such as benchmarking, performance analysis, and data segmentation.

Syntax

PERCENTILE(array, k)

• array: This parameter specifies the range of cells containing the data points you want to analyze.
• k: This represents the percentile value you want to find, expressed as a decimal between 0 and 1, where 0.5 corresponds to the 50th percentile (the median).

Example #1

PERCENTILE(A1:A10, 0.25)

Calculates the 25th percentile of the values in cells A1 through A10, which indicates that 25% of the data points fall below this value. For example, if the data contains values like {10, 20, 30, 40, 50, 60, 70, 80, 90, 100}, the result would be 32.5.

Example #2

PERCENTILE(B1:B20, 0.9)

Determines the 90th percentile for the data in cells B1 to B20, meaning 90% of the values are below this threshold. If the dataset is {5, 15, 25, 35, 45, 55, 65, 75, 85, 95, 105, 115, 125, 135, 145, 155, 165, 175, 185, 195}, the result would be 175.

Example #3

PERCENTILE(C1:C15, 0.5)

Finds the median (50th percentile) of the range C1 to C15, where half of the values lie below it. For instance, with the values {1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29}, the result would be 15.

Error handling

• NUM!: This error occurs if the k value is less than 0 or greater than 1, as it is not a valid percentile.
• REF!: This indicates that the specified array includes cells that are not valid references.
• N/A: This error signifies that the requested percentile cannot be calculated, often occurring when the array is empty or contains non-numeric values.

Conclusion

The PERCENTILE function is invaluable for statistical analysis in Excel, providing insights into data distributions and enabling better decision-making. By understanding how to apply this function, users can effectively interpret their datasets and leverage this information for strategic advantages.