Which test statistic to use for Wilcoxon signed rank test when n < 25 The z-statistic is based on an approximation of the normal distribution.
When n samples are equal or greater than 25, it could be argued that a sample is asymptotically normal, and that a z-statistic can be used to report the Wilcoxon signed-rank test output.
For n < 25, where the summed rank value may not be approximated by the normal distribution, what would you report as the test statistic for the Wilcoxon signed-rank test?
To note: I am working in R and using the wilcox.test function, which reports the test statistic V.
 A: As long as you make it plain which statistic you use it shouldn't matter which of several equivalent statistics you use - including a standardized statistic. They're each giving the same information about the sample, and the exact p-value for each would be identical. 
[Using a normal approximation for any of the versions of the statistic would give a slightly different p-value to the exact p-value, but this is a separate issue. The normal is pretty good down a fair bit smaller than n=25 - with a continuity correction.] 
I'd suggest choosing to report whichever version of the statistic you expect that your intended audience will be most familiar with. 
Unless there was a clear reason to do otherwise, I'd probably just report the statistic given by the package I was using (after specifying the statistic sufficiently clearly that it wasn't ambiguous).
A: As a software note, the wilcox.test function in R does not return the Z value. If the sample size is less than 50 and there are no ties, by default the software computes the p value with an "exact" method, and doesn't compute a Z value at all.  In other cases, the function computes the Z value but doesn't report it. 
A = c(1,3,5,7,9)
B = c(2,4,6,8,10)

wilcox.test(A, B, exact=TRUE)

   ### Wilcoxon rank sum test
   ###
   ### W = 10, p-value = 0.6905

wilcox.test(A, B, exact=FALSE, correct=FALSE)

   ### Wilcoxon rank sum test
   ### 
   ###  W = 10, p-value = 0.6015

If one were interested in the Z value, I know a couple of methods to extract it in R.  One is to use the coin package.  Another is to use the rcompanion package (with the caveat that I am the author of that package.)
if(!require(coin)){install.packages("coin")}
if(!require(rcompanion)){install.packages("rcompanion")}

Y = c(A, B)
Group = c(rep("A", length(A)), rep("B", length(B)))
Data=data.frame(Group, Y)

library(coin)

wilcox_test(Y ~ Group, data=Data)

   ### Asymptotic Wilcoxon-Mann-Whitney Test
   ###
   ### Z = -0.52223, p-value = 0.6015

library(rcompanion)

wilcoxonZ(A, B, exact=FALSE, correct=FALSE)

   ###      z 
   ### -0.522

wilcoxonZ(A,B, exact=TRUE)

   ###      z 
   ###     NA

The Z value itself doesn't give any more information than the p value does.  However, sometimes Z / sqrt(N) is used as an effect size statistic, often called r.
-0.522 / sqrt((length(A) + length(B)))

   ###  -0.1650709

library(rcompanion)

wilcoxonR(x=Y, g=Group)

   ###      r 
   ### -0.165 

