A favorite example in theoretical statistics is this:
A sample of individuals are drawn independently from a distribution with density $f(x)$, where $f(x)$ is unknown, but is known to be symmetric about an unknown center $\mu$. Estimate $\mu$.
To explain what I mean by "a favorite example", I'll list a few papers that provide procedures for this problem. Huber's original paper on robust estimators discusses this problem at length. In Diaconis and Freedman's paper about inconsistent Bayes estimates, one of the results is about a Bayesian prior over possible symmetric density functions $f$. The literature on the empirical characteristic function includes applications to this problem, here and here.
However, I can't think of situations where this happens in practice. I know, of course, that people often make normal assumptions, and the normal distribution is symmetric. However, if you're looking for estimators like these that don't assume specific distributions, presumably you think there are deviations from a normal assumption, or other common distributional assumptions. But I can't think of a reason one would assume that these deviations happen equally on both sides.
So, my question is: what situation justifies the assumption of symmetry, but not the assumption of a specific form of a distribution?