Can I use skewness to determine statistical significance of tail differences?

In summary, to determine if the difference in the negative side of the tail of two distributions is statistically significant, you can compare their skewnesses using a t-test or an F-test.
  • #1
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Homework Statement


I have two distributions that I'm comparing. They are probably closest in resemblance to a logistic distribution. Anyway my problem is that I am wanting to determine if the difference in the negative side of the tail is statistically signifcant.

The Attempt at a Solution


I've calculated the skewness of the two distributions and i get -0.493 and -0.620 . But I really don't know how I would determine if it is statistically significant. Is there a test I can apply to determine this?

I'm sorry if this is a really stupid question, but I don't have any good sources (books or otherwise) that explore this.

Thanks in advance for any help.
 
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  • #2
The skewness of a distribution is a measure of its asymmetry, so comparing the skewnesses of two distributions can tell you if the difference in their tails is statistically significant. You can use a t-test or an F-test to compare the skewnesses of the two distributions and determine if the difference is statistically significant. The t-test is used if you have two samples of data from the two distributions, while the F-test is used if you have one sample of data from each distribution.
 

Related to Can I use skewness to determine statistical significance of tail differences?

What is statistical significance?

Statistical significance refers to the likelihood that the results of an experiment or study are not due to chance or random error. It is a measure of how confident we can be in our findings and conclusions.

How is statistical significance determined?

Statistical significance is typically determined by conducting a hypothesis test, where the null hypothesis (the hypothesis that there is no significant difference between groups or conditions) is compared to the alternative hypothesis (the hypothesis that there is a significant difference between groups or conditions). If the p-value is below a pre-determined threshold (usually 0.05), then the results are considered statistically significant.

What is the difference between statistical significance and practical significance?

Statistical significance refers to whether the results are likely due to chance, while practical significance refers to whether the results are meaningful or useful in a real-world context. A study can be statistically significant but not practically significant, meaning that although there is a significant difference between groups, it may not be a significant enough difference to have a practical impact.

Why is statistical significance important in research?

Statistical significance is important in research because it helps to determine whether the results of a study are reliable and not due to chance. It also allows researchers to make inferences about a larger population based on the results of a smaller sample. Without statistical significance, it is difficult to draw meaningful conclusions from a study.

What are some limitations of statistical significance?

One limitation of statistical significance is that it does not necessarily indicate the strength or magnitude of the difference between groups. A study may be statistically significant, but the difference between groups may be small. Additionally, statistical significance is affected by sample size, so a study with a larger sample size may be more likely to find a statistically significant result, even if the difference between groups is small. It is important to consider both statistical and practical significance when interpreting research findings.

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