Tognini-Bonelli (2001) made the following distinction between corpus-based and corpus-driven studies. While corpus-based studies start with pre-existing theories which are tested using corpus data, in corpus driven studies the hypothesis is derived by examination of the corpus evidence. This chapter will give an overview of the two different families of statistical tests which are suited for these two approaches. For corpus-based approaches, we use more traditional statistics, such as the t-test, or ANOVA which return a value called a p-value to tell us to what extent we should accept or reject the initial hypothesis. Multi-level modelling (also known as mixed modelling) is a new technique which shows considerable promise for corpus-based studies, and will also be described here to analyse the ENNTT subset of Europarl corpus. Multi-level modelling is useful for the examination of hierarchically structured or “nested” data, where for example translations may be “nested” together in a class if they have the same language of origin. A multi-level model takes account both of the variation between individual translations and the variation between classes. For example, we might expect the scores (such as vocabulary richness or readability scores) of two translations in the same class to be more similar to each other than two translations in different classes.