We consider the likelihood ratio test of a simple null hypothesis (with density f0 ) against a simple alternative hypothesis (with density g0 ) in the situation that observations Xi are mismeasured due to the presence of measurement errors. Thus instead of Xi for i=1,…,n, we observe Zi=Xi+δ√Vi with unobservable parameter δ and unobservable random variable Vi . When we ignore the presence of measurement errors and perform the original test, the probability of type I error becomes different from the nominal value, but the test is still the most powerful among all tests on the modified level. Further, we derive the minimax test of some families of misspecified hypotheses and alternatives. The test exploits the concept of pseudo-capacities elaborated by Huber and Strassen (1973) and Buja (1986). A numerical experiment illustrates the principles and performance of the novel test.