After some theoretical discussion on the issue of representativity of a corpus, this paper presents a simple yet very efficient technique serving for (semi-)automatic detection of those positions in a part-of-speech tagged corpus where an error is to be suspected. The approach is based on the idea of learning and later application of "negative bigrams", i.e. on the search for pairs of adjacent tags which constitute an incorrect configuration in a text of a particular language (in English, e.g., the bigram ARTICLE - FINITE VERB). Further, the paper describes the generalization of the "negative bigrams" into "extended negative bigrams of length n", for any natural n, which indeed provides a powerful tool for error detection in a corpus. The approach is illustrated throughout on the case of the NEGRA corpus  (hence some command of German might be helpful, even though not really necessary). Finally, some general implications for statistical taggers are mentioned.