Uncertainty of Sample skew and kurtosis

  • Thread starter DethLark
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In summary, the uncertainty of a sample's skewness and kurtosis can be calculated using the standard error, which is dependent on the sample size (N). However, this does not follow the same behavior as the uncertainty of the standard deviation. The terms for the variance and standard deviation of skewness and kurtosis are more obscure, but can be calculated using the sample size and standard deviation. It is important to clarify whether you are referring to statistics of a population or estimators of those statistics when using these formulas.
  • #1
DethLark
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What is the uncertainty of a samples skewness and kurtosis? Such as the uncertainty of the standard deviation is SD/sqrt(2*(N-1)). I was able to find what someone is calling the Standard Error of these but they both only depend on N which doesn't make sense to me.

Skewness Standard Error: sqrt((N^2-1)/((N-3)*(N+5)))

Kurtosis Standard Error: SSE*sqrt(6*N*(N-1)/((N-2)*(N+1)*(N+3)))

These get close to 1 in the range of measurements that I'm looking at which doesn't have the same behavior of the uncertainty of the standard deviation, which goes from inf to 0 with increasing measurements. And it doesn't depend on the value of the skew or kurtosis...
 
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  • #2
Turns out the terms I were looking for are either the variance of sample skewness and kurtosis, or standard deviation of skewness or kurtosis. I have found the following:

Std. Dev. of Skewness: sqrt((6*(N-2)*(Std. Dev.)^2)/((N+1)*(N+3)))
Std. Dev. of Kurtosis: sqrt((24*N*(N-2)*(N-3)*(Std. Dev.)^2)/((N+1)^2*(N+3)*(N+5)));

I hope these are correct as they seem rather obscure (which I can see by the fact no one answered) though I'd think they should be much more well known.
 
  • #3
You should clarify whether you are asking about statistics of a population or about estimators of those statistics. For example, in the formula you gave for the standard deviation of the sample skewness, you'd have to know the population standard deviation exactly in order to apply it. If you don't know the statistics of the population exactly and only have sample data then all you know is the sample standard deviation.
 

1. What is sample skew and kurtosis?

Sample skew and kurtosis are statistical measures that describe the shape of a data distribution. Skewness measures the degree to which a distribution is asymmetrical, while kurtosis measures the degree of peakedness or flatness of the distribution.

2. Why is it important to consider the uncertainty of sample skew and kurtosis?

Just like any other statistical measure, sample skew and kurtosis are subject to uncertainty due to the random sampling process. It is important to consider this uncertainty in order to accurately interpret the results and make informed conclusions about the data.

3. How is the uncertainty of sample skew and kurtosis calculated?

The uncertainty of sample skew and kurtosis can be calculated using mathematical formulas that take into account the sample size and the variability of the data. These formulas are based on the central limit theorem, which states that the sampling distribution of a statistic tends towards a normal distribution as the sample size increases.

4. What does a high or low uncertainty of sample skew and kurtosis indicate?

A high uncertainty of sample skew and kurtosis indicates that the calculated values are less precise and have a wider range of potential values. This could be due to a small sample size or high variability in the data. On the other hand, a low uncertainty indicates that the calculated values are more precise and have a smaller range of potential values.

5. How can the uncertainty of sample skew and kurtosis impact the results of a study?

The uncertainty of sample skew and kurtosis can impact the results of a study by affecting the interpretation of the data. If the uncertainty is high, it may be difficult to draw definitive conclusions about the shape of the distribution. Additionally, if the uncertainty is not taken into account, it could lead to inaccurate or misleading conclusions.

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