SKEWP in Collabora Office: Calculate Population-Moment Skewness
Use Collabora SKEWP to compute population-standardized third moments and compare them with adjusted sample skewness.
Verification status: See the evidence and test scope described in this guide. How formula examples are checked
Quick answer: =SKEWP(2;3;1;6;8;5) has documented result approximately 0.2329985562. SKEWP uses the population-moment convention rather than SKEW’s finite-sample adjustment.
Syntax and minimum observations
=SKEWP(Argument1 [; Argument2 ...])
The dedicated Collabora help permits up to 255 arguments, including values, references and ranges. It specifies at least three usable numeric values and says that text and empty cells within a data range are ignored.
The current source subtracts the mean, divides deviations by the population standard deviation, cubes the standardized deviations and averages the cubes. It still enforces the documented minimum count, even where a mathematical moment expression could be written for a smaller nonconstant dataset.
SKEWP is not a request to infer the entire population from a sample automatically. Its name identifies the calculation’s convention; the analyst must decide what the available observations represent.
Example 1: reproduce the official six-value example
Put 2, 3, 1, 6, 8 and 5 in A2:A7. Enter =SKEWP(A2:A7) in C2. The independent target is approximately 0.232998556.
The mean is 25/6, approximately 4.166666667. The population variance is 209/36, approximately 5.805555556. These differ from a sample variance using n-1 in its denominator.
A transparent check averages the cubes of (value-mean)/population_standard_deviation over all six observations. Do not substitute the sample standard deviation while retaining the population averaging formula; that creates a different quantity.
Example 2: contrast adjusted sample skewness
For the same range, =SKEW(A2:A7) has independent target approximately 0.319046413. The ratio between adjusted and population-moment skewness is SQRT(n*(n-1))/(n-2), approximately 1.369306394 for n=6.
This adjustment can matter in small datasets. A disagreement between the two functions is expected and should not be “fixed” by deleting observations or switching names without considering the required definition.
Record the exact function used in any reproducible calculation. A generic label such as asymmetry is not enough to reconstruct its finite-sample convention.
Advanced application: test symmetric and asymmetric controls
For 1, 2, 3, 4 and 5, SKEWP has independent result 0. For 1, 1, 1, 1 and 6, it has result 1.5.
Negating the asymmetric dataset gives -1.5, while adding a constant to every observation leaves the original positive result unchanged. These transformations test the third-moment direction and centering.
A zero third moment does not guarantee that every possible dataset is symmetric. Different asymmetries can cancel in a single summary. Inspect the distribution and original values rather than treating one statistic as a complete description.
Advanced application: audit unit conversion
Multiplying every value in the official six-value dataset by two preserves its independent SKEWP result approximately 0.232998556. Dividing all values by a positive constant likewise preserves it.
The statistic is dimensionless, unlike a raw third central moment whose units are cubed. A negative scale reverses the sign rather than preserving it.
When a positive unit conversion appears to change skewness, compare the observation count and stored values first. Rounding one dataset much more aggressively than the other or converting only some rows changes the data beyond a uniform scale transformation.
Errors and troubleshooting
Current source enforces at least three numeric observations and rejects zero population standard deviation. The count failure follows a division-by-zero path; the zero-spread failure follows an illegal-argument path. These are source statements rather than native localized error captures.
Text and blanks in a referenced range are ignored, while numeric zero is included. A selected range with three cells may therefore still have fewer than three usable observations. The guide does not generalize this reference behavior to every direct string, array or imported Boolean state.
The official help identifies SKEWP with OpenFormula 1.2. That format history does not establish the first Collabora release that implemented it. Extreme-value numerical behavior and native file round trips remain unverified.
Availability and verification
The exact function is documented in Collabora Office 26.04 desktop help. The cited help snapshot was retrieved on 2026-10-15 and hash-verified October 15, 2026. Desktop documentation does not establish Online or mobile support. First introduction and last functioning release are unknown in this evidence.
No native Collabora Office execution occurred. All numerical results are independent mathematical expectations, with English names and semicolon argument separators. Native error displays, all coercions and extreme numerical behavior are not certified.
Implementation statements are corroborated by Collabora current engine source, retrieved October 15, 2026. This is moving-main source evidence, not a pinned desktop 26.04 binary or execution.