In the last few years, there has been a growing number of studies addressing the Text Simplification (TS) task as a monolingual machine translation (MT) problem which translates from ‘original’ to ‘simple’ language. Motivated by those results, we investigate the influence of quality vs quantity of the training data on the effectiveness of such a MT approach to text simplification. We conduct 40 experiments on the aligned sentences from English Wikipedia and Simple English Wikipedia, controlling for: (1) the similarity between the original and simplified sentences in the training and development datasets, and (2) the sizes of those datasets. The results suggest that in the standard PB-SMT approach to text simplification the quality of the datasets has a greater impact on the system performance. Additionally, we point out several important differences between cross-lingual MT and monolingual MT used in text simplification, and show that BLEU is not a good measure of system performance in text simplification task.