PERCENTILE.INC
Returns the k-th percentile of values in a range, where k is in the range 0 to 1 inclusive. Modern replacement for the legacy PERCENTILE function.
PERCENTILE.INC(array, k)Arguments
arrayThe array or range of data that defines relative standing.kThe percentile value (0 ≤ k ≤ 1).
=PERCENTILE.INC({1,2,3,4,5}, 0.5)50th percentile (median) = 3
=PERCENTILE.INC({1,2,3,4,5}, 0.9)90th percentile
- •Available since Excel 2010 — same result as the legacy PERCENTILE
- •Returns #NUM! if k < 0 or k > 1, or if the array is empty
- •Use PERCENTILE.EXC for the exclusive variant (0 < k < 1)
- •Confusing the k argument's format - it must be a decimal between 0 and 1 (like 0.9 for the 90th percentile), and entering 90 instead of 0.9 raises a #NUM! error
- •Using PERCENTILE.INC when PERCENTILE.EXC would better match a specific statistical convention your organization or textbook uses - the two can give noticeably different results, especially near the edges of small datasets
- •Applying PERCENTILE.INC to a very small dataset and expecting a value that actually exists in the data - percentile calculations often interpolate between two existing values, producing a result that isn't literally present in the range
PERCENTILE.EXCCalculates a percentile excluding the range's endpoints, a stricter alternative to PERCENTILE.INC's inclusive method.QUARTILE.INCCalculates specific percentiles (0th, 25th, 50th, 75th, 100th) using simpler, preset arguments instead of PERCENTILE.INC's fully flexible k value.MEDIANEquivalent to PERCENTILE.INC with k set to 0.5, a more direct way to get specifically the middle value.Why does PERCENTILE.INC return a #NUM! error?
The k argument is likely outside the valid 0 to 1 range - use 0.9, not 90, for the 90th percentile.
What's the difference between PERCENTILE.INC and PERCENTILE.EXC?
PERCENTILE.INC includes the full 0 to 1 range for k and matches the legacy PERCENTILE function's behavior. PERCENTILE.EXC restricts k to strictly between 0 and 1, following a different statistical convention that treats the dataset's endpoints differently.
Why doesn't PERCENTILE.INC return a value that's actually in my data?
Percentile calculations often interpolate between the two nearest values in the sorted dataset, so the result can fall between existing data points rather than matching one exactly.
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