Chi-square test on a sample from population having an unequal distribution I got a sample with an unequal gender distribution ($30$ males to $200$ female). This distribution is quite "representative" for the population the sample is taken from.
Nevertheless, in a paper, does it make any sense to report chi-square for gender (with the purpose to show this inequality beyond descriptives)?
Intuitively, I would say no, because the assumption of equality is already not met in population. Hence, it would be some kind of circular reasoning.
Am I right?
 A: I think this means you're comparing $30/200$ with a hypothesis of equal frequencies implying expected frequencies $115/115$. 
My calculations give Pearson chi-square of $125.7$ and a $P$-value of about $4 \times 10^{-29}$: if that's not strong enough to convince the most sceptical of chance sceptics (someone asserting that you could have got this breakdown just by chance by sampling a population with equal frequencies), then your case is hopeless. 
That chi-square test makes perfect sense, but doesn't sound interesting or useful. Sometimes, indeed often, descriptive statistics stand and speak for themselves and do not need elaborate tests of the obvious in support. There is no need to test ridiculous hypotheses, or hypotheses you don't entertain seriously. 
There is no circularity in reporting that your sample data echo what is broadly known about the population; it's no more circular than reporting that most of the people attending a ballet school are female. 
As @Scortchi underlines, a better way of quantifying uncertainty here is using a confidence interval. The proportion of females is $0.87$ and $95$% confidence intervals run from about $0.82$ to $0.91$, regardless of how you calculate them. That interval is clearly a long way from $0.5$. 
These calculations are standard, but for the record I used chitesti and cii in Stata. The Stata manual entry on these confidence interval calculations is accessible to all and gives literature references on different methods for binomial confidence intervals. For probabilities very near $0$ or $1$, it matters what method you use, but that does not bite for this example. 
