As every year around this time, university campuses are buzzing with students who are starting their studies or returning to the campus after the summer break – this incredible transformation pours life into buildings – empty spaces become lecture theatres, seminar rooms and labs. Students have the opportunity to learn many new things about the subject they chose to study and also engage with the academic environment more generally.  Among the educational and development opportunities students have at the university one transferable skill stands out: statistical literacy.

Numbers are an essential part of our everyday life. We count the coins in our pocket, the minutes before the next bus arrives or the sunny days in a rainy year. Numbers and quantitative information are also very important for students and educators. Statistical literacy – the ability to produce and interpret quantitative information – belongs to the basic set of academic skills that, despite its importance, may not always receive the attention it deserves.

Many students (and academics) are afraid of statistics – think about what your first reaction is to the equation in Figure 1 below.

Figure 1: The equation of standard deviation (mathematical form)

This is because statistics is often misconstrued as the art of solving extremely complicated equations or a mysterious magic with numbers. Statistics, however, is first and foremost about understanding and making sense of numbers and quantitative information. For this, we need to learn the basic principles of collecting, organising and interpreting quantitative information. Critical thinking is thus much more important for statistics than the number crunching ability. After all, computers are very good at processing numbers and solving equations and we can happily leave this task to them. For example, many even complex statistical tasks can be achieved by using tools such as the Lancaster Stats Tool online, where the researcher can merely copy-paste their data (in an appropriate format) and press one button to receive the answer.

Humans, on the other hand, outperform computers in the interpretation skills. This is because we have the knowledge of the context in which numbers appear and we can therefore evaluate the relative importance of different quantitative results. We as teachers, linguists, sociologists, scientists etc. can provide the underlying meaning to numbers and equations and relate them to our experience and the knowledge of the field. For example, the equation in Figure 1 can be simplified as follows:

Figure 2: The equation of standard deviation (conceptual)

When we relate this to what we know about the world, we can see that the question we are asking in Figure 2 is how much variation there is in our data, a question about variability, difference in tendencies and preferences and overall diversity. This is something that we can relate to in our everyday experience: Will I ever find a twenty-pound note in my pocket? Is the wait for the bus longer in the evening? Is the number of sunny days different every year? When talking about statistics in education, I consider the following point crucial: as with any subject matter, it is important to connect statistical thinking and statistical literacy with our daily experience.

To read more about statistics for corpus linguistics, see Brezina, V. (2018). Statistics in Corpus Linguistics: A Practical Guide. Cambridge University Press.

Vaclav Brezina and Gabriele Pallotti

Inflectional morphology has to do with how words change their form to express grammatical meaning. It plays an important role in a number of languages. In these languages, the patterns of word change may for example indicate number and case on nouns, or past, present and future tense on verbs. For example, to express the past participle in German we regularly add the prefix ge- and optionally modify the base. Ich gehe [I go/walk] thus becomes Ich bin gegangen [I have walked].  English also inflects words (e.g. walk – walks – walking – walked; drive – drove – driven) but the range of inflected forms is narrower than in many other languages. The range of morphological forms in a text can be seen as its morphological complexity. Simply put, it is an indicator of the morphological variety of a text, i.e. how many changes to the dictionary forms of the words are manifested in the text.

To find out more about morphological complexity, how it can be measured and how L2 speakers acquire it, you can read:

• Brezina, V. & Pallotti, G. (2016). Morphological complexity in written ESL texts. Second Language Research, Advance Access [OPEN ACCESS].
• Pallotti, G. (2015). A simple view of linguistic complexity.Second Language Research 31 (1), pp. 117-134.

Gabriele Pallotti and I have been working together to investigate the construct and develop a tool that can analyse the morphological complexity of texts. So far, the tool has been implemented for English, Italian and German verbal morphology. Currently, together with Michael Gauthier from Université Lyon we are implementing the morphological complexity measure for French verbs.

To analyse a text in the Morphological complexity tool, copy/paste the text in the text box, select the appropriate language and press ‘Analyse text now’ (Fig. 1).

Figure 1. Morphological tool: Interface

The tool will output the results of the linguistic analysis that highlights all verbs and nouns in the text and identifies morphological changes (exponences). After clicking on the ‘Calculate MCI’ button the tool also automatically calculates the Morphological Complexity Index (MCI) – see Fig. 2.

Figure 2. Morphological tool output: Selected parts