A permutation t-test proves useful when the assumption of 'regular' t-test are not met. In
particular, when the two groups being compared show a very skewed distribution, and when the
sample sizes are very unbalanced.
"The permutation test is useful even if we plan to use the two-sample t test. Rather than relying
on Normal quantile plots of the two samples and the central limit theorem, we can directly check
the Normality of the sampling distribution by looking at the permutation distribution.
Permutation tests provide a <U+201C>gold standard<U+201D> for assessing two-sample t tests. If the two P-values
differ considerably, it usually indicates that the conditions for the two-sample t don<U+2019>t hold for
these data. Because permutation tests give accurate P-values even when the sampling distribution
is skewed, they are often used when accuracy is very important." (Moore, McCabe, Craig,
"Introduction to the Practice of Statistics", New York: W. H. Freeman and Company, 2009).