*The Foundations of Statistic*s provides a step-by-step guide to introductory statistics. Just browsing through the book one can immediately see that this is not a mathematics textbook of the sort everyone is so used to. Rather, as the authors intended, this is an introductory text for non-mathematicians, specifically those in the field of linguistics. However, I would not stop there. The topics presented make it also relevant for students in other fields of the social sciences.

The book provides a very non-theoretical and non-technical introduction to topics in statistics such as randomness and probability, the sampling distribution of the sample mean, power, ANOVA, Linear Models and Linear Mixed Models.

The book is saturated with R code. Going through the book, one gets quite comfortable with R, but I believe the learning curve is still very steep, so that the main hurdle for the reader is the use of R. However, considering the functionality of R itself and the availability of applications supporting R, this cannot be seen as an obstacle.

The book starts off going very slowly through the R code, explaining each of the ideas. The book tries to provide hand-on experience with simulated data, leading to understanding definitions and theorems. All the topics are presented in the following way: simulate a random sample of certain size and analyse it: then increase the sample size and/or change some parameters and see what happens. This process is further enhanced with graphical output for majority of R queries and narrative explanations why certain steps were undertaken, which greatlly aids in understanding.

The pace is rather slow at the beginning, but picks up a little bit starting with ANOVA chapter. Problems are presented at the end of each chapter and are of an applied nature. However, the number of problems is rather small.

Overall, I believe the book serves its intended purpose very well. I would happily use it in undergraduate courses for social science students. But perhaps this book would even be helpful as a supplement for students in mathematics and statistics, because it can help students "see the light" behind theorems.

Ita Cirovic Donev holds a Masters degree in statistics from Rice University. Her main research areas are in mathematical finance; more precisely, statistical methods for credit and market risk. Apart from the academic work she does statistical consulting work for financial institutions in the area of risk management.