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I have created a plot of the regression slope of sea surface temperatures (x) and an atmospheric variable (y). Although, I need to test the statistical significance of these trends using a non-parametric test (doesn't assume data is normally distributed). Specifically, I am trying to use the Mann-Whitney U-test as it was suggested by a reviewer (but open to whatever will work and allow me to compare to results from the Students T-test). To do this, I have already calculated the regression slope to compare to a situation when the slope is zero. Then, I created an array of zeros that is the same size as my regression array to put into the statistical function:

### Try out the Mann-Whitney U-test: #### DOESNT WORK RIGHT NOW!!!!! 
zero = np.zeros(np.shape(regression))   ### shape: (721,1440)
test = mannwhitneyu(zero, regression)   ### shape: (721,1440)

Although, after running the test, I end up with an array of only one dimension and size 1440:

psave = test[1]   ### This array is only a single dimension (1440)

Ultimately, I would like to end up with an array of p-values from a non-parametric test of the shape (721,1440) testing the significance of my regression slope. Thank you for any and all help!!

Megan Franke
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  • Mann-Whitney U-test compares two populations. I'm not sure how the regression data/results can be framed in this way. Can you provide more details on the test intent and/or context given by the reviewer? – Marat Aug 29 '23 at 15:42
  • The intent of the test is to mark grid points in which the regression slope is statistically significant from zero (or no regression slope) on gridded data. To start, I had 3D data (time, lat, lon), leaving only lat and lon after the regression. Specifically, my reviewer commented that as my data is not normally distributed, I cant use the Students T-test and was instead recommended to use a non-parametric test for significance (i.e. Mann-Whitney U-test). – Megan Franke Aug 29 '23 at 20:13

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