Reconciling seemingly different null hypotheses for Mann-Whitney test The Mann-Whitney test ( Rank sum, Wilcoxon-Mann-Whitney ) is according to some authors
testing 
H0: the populations from which the 2 samples were drawn are identical in every respect
e.g. see Hoel Introduction to Mathematical Statistics, 5th Edition, p342 and also on this site
Mann-Whitney test interpretation
Other times it is described as 
H0: one variable is stochastically larger than the other
indeed, that is the essence of the title of the Mann-Whitney paper of 1947 in Annals of Mathematical Statistics.
and also on this site Interpretation of Wilcoxon Rank Sum test results
One can perform a quick numerical experiment (R)
set.seed(123)

n <- 10

x1 <- rnorm(n, mean=0, sd=1)
x2 <- rnorm(n, mean=0, sd=3)

print( wilcox.test(x1,x2))

yielding the output
    Wilcoxon rank sum test

data:  x1 and x2
W = 39, p-value = 0.4359
alternative hypothesis: true location shift is not equal to 0

Note x2 has 3 times the sd of x1 so the populations are not identical. Yet, the test, if I am interpreting the result correctly, claims no evidence against H0.
If the first view of H0 holds, why doesn't the test
reject the hypothesis the populations are identical.
These 2 views of H0 do not seem equivalent. How are they reconciled? 
 A: I don't know if this clears it up, but this is how Conover and Wilcox handle the hypotheses for the Mann-Whitney test.
Conover, 1999, Practical Nonparametric Statisitcs, 3rd.

Let F(x) and G(x) be the distribution functions corresponding to X and Y, respectively.  Then the hypotheses may be stated as follows.  H0: F(x)=G(x) for all x.  H1: F(x)≠G(x) for some x. 
... In many real situations any difference between distributions implies that P(X>Y) is no longer equal to 1/2. Therefore, H1: P(X>Y) ≠ P (X [less than] Y) is often used instead of the above.

Wilcox, 2017, Modern Statistics for the Social and Behavioral Sciences A Practical Introduction, 2nd

Let p be the probability that a randomly sampled observation from the first group is less than a randomly sampled observation in the second. H0: p = 0.5 (7.24)
... In a very real sense, a more accurate description of the Wilcoxon-Mann-Whitney test is that it provides a test of the hypothesis that the two distributions are identical. ... When the two distributions are identical, a correct estimate of the standard error [...] is being used. But otherwise, under general conditions, an incorrect estimate is being used, which results in practical concerns, in terms of both Type I errors and power, when using Equation (7.24) to test H0:p=0.5. 

