Bodo Winter (2019). Statistics for linguists: an introduction using R. New York & London: Routledge. pp. xvi + 310.

Statistical skills used not to be a prerequisite for doing phonological analyses in the generative tradition. In fact, the field was dominated by a strong belief that grammatical knowledge (competence) is independent of the statistical patterns observed in the lexicon (e.g. Chomsky 1957, Halle 1978). This situation has changed rapidly in recent years, as a result of a growing body of studies that have demonstrated that phonological knowledge is – or at least can be – stochastic, partly reflecting statistical trends in the lexicon (e.g. Ernestus & Baayen 2003, Hayes & Londe 2006). Another reason for the increased use of statistical methods is the rise of interest in employing experimental techniques and corpus data for phonological argumentation, a general approach now known as ‘laboratory phonology’ (Beckman & Kingston 1990, Pierrehumbert et al. 2000). It is safe to say that familiarity with statistical techniques has now become a desirable skill for theoretical phonologists. One can of course take the position that phonological knowledge is strictly dichotomous, that (lexical) statistics has nothing to do with phonological competence or that evidence from experimental and corpus studies is irrelevant for phonological theorisation. Nevertheless, in order to evaluate how quantitative data and analyses bear upon questions about phonological competence, I believe that it is crucial that we have some basic understanding of statistics. To take an example, MaxEnt Harmonic Grammar has recently been used to model various aspects of linguistic knowledge (e.g. Goldwater & Johnson 2003, Hayes & Wilson 2008, Shih 2017, Breiss & Hayes 2020), and is mathematically equivalent to (multinomial) logistic regression (Jurafsky & Martin 2019). Whether we endorse or reject it as a theory of grammar, we need to understand it, and to do so, it is necessary to know how regression works in general. There are already several statistics textbooks written for linguists, including Baayen (2008), Johnson (2008), Gries (2013) and Levshina (2015). Nevertheless, Winter’s book is a very welcome addition to the field. I thoroughly enjoyed reading the book, and learned a lot from it.