We study the attitude of decision makers to skewed noise. For a binary lottery that yields the better outcome with probability $p$, we identify noise around $p$, with a compound lottery that induces a distribution over the exact value of the probability and has an average value p. We propose and characterize a new notion of skewed distributions, and use a recursive non-expected utility model to provide conditions under which rejection of symmetric noise implies rejection of skewed to the left noise as well. We demonstrate that rejection of these types of noises does not preclude acceptance of some skewed to the right noise, in agreement with recent experimental evidence. We apply the model to study random allocation problems (one-sided matching) and show that it can predict systematic preference for one allocation mechanism over the other, even if the two agree on the overall probability distribution over assignments. The model can also be used to address the phenomenon of ambiguity seeking in the context of decision making under uncertainty.