I want to compare the median of body temperature (passem$temp) across two groups (whether the patient died or not, died30in.numeric). I ran Mann Whitney Wilcoxon first in R using this command wilcox.test(passem$temp, died30in.numeric) and got these results:

> wilcox.test(passem$temp, died30in.numeric)

    Wilcoxon rank sum test with continuity correction

data:  passem$temp and died30in.numeric
W = 10719076, p-value < 2.2e-16
alternative hypothesis: true location shift is not equal to 0

However, when I ran the test in Stata, I got the following results which were different from that in R:

ranksum temp, by(died30in)

Two-sample Wilcoxon rank-sum (Mann–Whitney) test

    died30in |      Obs    Rank sum    Expected
           0 |     2987   4924500.5   4891212.5
           1 |      287    436674.5    469962.5
    Combined |     3274     5361175     5361175

Unadjusted variance   2.340e+08
Adjustment for ties  -746907.26
Adjusted variance     2.332e+08

H0: temp(died30in==0) = temp(died30in==1)
         z =  2.180
Prob > |z| = 0.0293

I also double check with SPSS, and the results were concordant with Stata analysis.

Of note, it might be an oversimplification (or misconception by some) to assume that Mann-Whitney-Wilcoxon is used to compare two medians; still, I want to conduct the test on our data.

What is the reason for this unexplained difference in results?


1 Answer 1


The reason for the dramatic difference is that you have run the Wilcoxon test incorrectly in R. You probably intended:

> wilcox.test(passem$temp ~ died30in)
  • $\begingroup$ Thank you so much, it now matches Stata and SPSS $\endgroup$
    – Abo7aneen
    Feb 23 at 3:42

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