This article discusses the effect of inconsistency in training data on the performance of text classifiers. Our experiments show that the inconsistency, even reaching a level as high as 34%, hardly affects the effectiveness of the classifiers. Better classifiers perform better independent of duplicates and label inconsistency. The implication is that past experiments (especially on the Reuters-21578 collection) remain valid. In the experiment process, the author proposes a duplicate detection technique that is far effective than previous ones. A new Chinese test collection for text categorization is also introduced for general free download.