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TEST_SKEW
Calculates the pvalue of the statistical test for the population skew (i.e. 3rd moment).
Syntax
TEST_SKEW(X)
X
is the data sample (a one dimensional array of cells (e.g. rows or columns)).
Remarks
 The data sample may include missing values (e.g. #N/A).

The test hypothesis for the population distribution skewness:
Where:
 is the null hypothesis^{i}.
 is the alternate hypothesis.
 is the population skew.

For the case in which the underlying population distribution is normal, the sample skew also has a normal sampling distribution:
Where:
 is the sample skew (i.e. 3rd moment).
 is the number of nonmissing values in the data sample.
 is the normal (i.e. Gaussian) probability distribution function.
 The sample data skew is calculated as:
Where:
 is the sample skew (i.e. 3rd moment)
 is the ith nonmissing value in the data sample.
 is the number of nonmissing values in the data sample.
 is the data sample standard deviation.
 In the case where the population skew is not zero, the mean is farther out than the median in the long tail.
The underlying distribution is referred to as skewed, unbalanced, or lopsided.  The underlying population distribution is assumed normal (Gaussian).
 This is a twosides (i.e. twotails) test, so the computed pvalue should be compared with half of the significance level ().
Examples
Example 1:
A  B  

1  Date  Data 
2  1/1/2008  #N/A 
3  1/2/2008  2.83 
4  1/3/2008  0.95 
5  1/4/2008  0.88 
6  1/5/2008  1.21 
7  1/6/2008  1.67 
8  1/7/2008  0.83 
9  1/8/2008  0.27 
10  1/9/2008  1.36 
11  1/10/2008  0.34 
12  1/11/2008  0.48 
13  1/12/2008  2.83 
14  1/13/2008  0.95 
15  1/14/2008  0.88 
16  1/15/2008  1.21 
17  1/16/2008  1.67 
Formula  Description (Result)  

=TEST_SKEW($B$2:$B$17)  pvalue of the test when skewness = 0 (0.4025)  
=SKEW($B$3:$B$17)  Sample skewness (0.1740) 