I couldn't think of a situation where you'd want to run a nonparametric test and return a parametric pvalue, or the alternative of running the parametric pearson test and returning the nonparametric test. Especially since if you ran these tests in Prism or R, pearson returns parametric pvalues, and spearman returns nonparametric pvalues, but there might be a case where the user would want this option? What do others think?

On Jul 9, 2013, at 2:46 PM, Greg Caporaso <notifications@github.com> wrote:

> In qiime/otu_category_significance.py:
> 
> >          if r:
> > -            result[OTU] = [r, prob, str(OTU_abundance_vals), str(category_vals)]
> > +            if corr_test == 'spearman':
> > +                result[OTU] = [r, nonparametric_p_val, \
> > +                     str(OTU_abundance_vals), str(category_vals)]
> > +            elif corr_test == 'pearson':
> > +                result[OTU] = [r, parametric_p_val, \
> We also have a non-parametric option for pearson - would it make sense to hook that up here?
> 
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