A Student’s Guide to Statistics Using R

Version: ef5ac6e

Preface

In every branch of the social sciences, from psychology to political science, statistics has become indispensable. The questions that motivate research in these fields—about how people think, behave, interact, and change—are complex, messy, and rarely yield to simple observation. This book was written to help students and researchers navigate that complexity. Our aim is to provide a clear, practical, and modern introduction to statistical reasoning and data analysis, showing how these tools can be used to turn imperfect, noisy data into credible, transparent answers to important social science questions.

We take a unified approach to the subject. Rather than treating each statistical method as an isolated “recipe” with its own set of rules, we frame all of them — whether t-tests, regression models, ANOVA, or Bayesian methods — as applications of the same underlying principles of modelling, estimation, and inference. This perspective makes it easier to see the connections between techniques, understand the logic behind them, and build the flexibility to tackle new analytical challenges beyond those covered in any one course or textbook. It also reflects how research is actually conducted: in real projects, statistical methods are chosen, adapted, and combined as part of a coherent modelling strategy.

The book is written for students who may be encountering statistics and data analysis for the first time, especially those in the social sciences who do not see themselves as “math people.” We recognise that formulas, symbols, and computer code can be intimidating, and we have worked to make the material as accessible as possible without oversimplifying it. Technical details are introduced only when they are needed, explained step by step, and always tied back to the substantive questions that motivate analysis. Examples are drawn from contemporary, high-quality research in psychology, sociology, economics, political science, and related fields, demonstrating not just how statistical methods work, but why they matter.

We also embrace the realities of doing research in the twenty-first century: statistics today is inseparable from computing. Throughout, we use the R programming language, a free and open-source platform for statistical computing and graphics. By learning R, readers gain not only an understanding of statistical methods, but also the practical skills to manage, analyse, and visualise data in ways that are transparent, reproducible, and adaptable to new problems. We guide readers through this process from first principles, so no prior programming experience is required.

Ultimately, our goal is not only to teach statistical techniques, but to cultivate a way of thinking — critical, model-based, and evidence-driven — that will serve readers throughout their academic and professional lives. Whether you plan to pursue a career in research, policy, industry, or any field where decisions must be made under uncertainty, the ability to reason with data is one of the most valuable skills you can have. We hope this book will help you develop that skill, see the power of statistical thinking, and perhaps even come to share some of our enthusiasm for it.