Can You Calculate a P-Value for Correlation Between Non-Parametric Data?

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The discussion centers on calculating a p-value for the correlation between two non-parametric data groups, A and B, with a Pearson correlation coefficient of 0.97. It is noted that Pearson's correlation is a parametric statistic, and for non-parametric data, Spearman's correlation should be used instead. The user provides additional information that both groups have 35 continuous samples and inquires about using a Student's t-test to predict the p-value. The response confirms that a t-test could be appropriate if assuming a parametric approach. Overall, the conversation emphasizes the importance of understanding the data type and experimental design when selecting statistical tests.
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I have made a correlation between group A and B and the pearsson coefficient constant, r, is at 0.97. The values are expression levels and hence they are non-parametric. I wonder how can i calculate the p-value of this correlation between group A and B? I know how to calculate the p-value for the difference between group A and B, but not for the correlation between group A and B.


Thanks for any inputs!
 
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Pearson's correlation coefficient is a parametric statistic. If your data require a non-parametric test (how did you determine this?), then you can't use Pearson's correlations, but should instead be using Spearman's correlation coefficient, which is the non-parametric counterpart to Pearson's. But, nobody will be able to help you beyond that without more information about the data set you're working with. What's your sample size? Do you have continuous or integer data, random or fixed variables, etc? Basically, without knowing your experimental design, nobody can recommend an appropriate statistical test.
 
Moonbear said:
Pearson's correlation coefficient is a parametric statistic. If your data require a non-parametric test (how did you determine this?), then you can't use Pearson's correlations, but should instead be using Spearman's correlation coefficient, which is the non-parametric counterpart to Pearson's. But, nobody will be able to help you beyond that without more information about the data set you're working with. What's your sample size? Do you have continuous or integer data, random or fixed variables, etc? Basically, without knowing your experimental design, nobody can recommend an appropriate statistical test.


Thanks for helping me out.

Both groups have 35 samples and the values are continues. Can i use student T test to predict this p-value, assumming that i use the parametric way?
 
sobored said:
Thanks for helping me out.
Both groups have 35 samples and the values are continues. Can i use student T test to predict this p-value, assumming that i use the parametric way?
I believe you can; as explained in this link or http://faculty.vassar.edu/lowry/rsig.html. (Both from a Yahoo! search on significance test for correlation coefficient.)
 
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I tried to combine those 2 formulas but it didn't work. I tried using another case where there are 2 red balls and 2 blue balls only so when combining the formula I got ##\frac{(4-1)!}{2!2!}=\frac{3}{2}## which does not make sense. Is there any formula to calculate cyclic permutation of identical objects or I have to do it by listing all the possibilities? Thanks
Since ##px^9+q## is the factor, then ##x^9=\frac{-q}{p}## will be one of the roots. Let ##f(x)=27x^{18}+bx^9+70##, then: $$27\left(\frac{-q}{p}\right)^2+b\left(\frac{-q}{p}\right)+70=0$$ $$b=27 \frac{q}{p}+70 \frac{p}{q}$$ $$b=\frac{27q^2+70p^2}{pq}$$ From this expression, it looks like there is no greatest value of ##b## because increasing the value of ##p## and ##q## will also increase the value of ##b##. How to find the greatest value of ##b##? Thanks
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