1  Introduction

1.1 What is ‘practicum’?

A practicum is a supervised, hands-on course of study. This book is a practicum in the fullest sense: it teaches skills that cannot be acquired by reading, only by doing. Every chapter ends with exercises that require you to sit at a keyboard and produce an artefact, whether that is a Git commit, a Docker container, or a finished analysis report.

The book is the companion to Statistical Computing in the Age of AI, the methods-focused volume. The two books cover complementary territory: the methods volume teaches what to compute and why; this volume teaches how to compute it reproducibly.

1.2 What this book covers

This book covers the practical craft of biostatistical data analysis:

1.3 What this book does not cover

  • The statistical theory underlying specific models (linear, generalised linear, mixed, survival). For that, see the companion book Statistical Computing in the Age of AI or a standard reference.
  • Experimental design, sample-size calculation, or causal inference beyond what is needed to execute an analysis plan.
  • Specialised computing platforms (Bioconductor, Stan, deep-learning frameworks) beyond brief mention.
  • Statistical theory in any depth.

The trade-off is deliberate. The book aims for breadth across the workflow rather than depth on any one method.

1.4 Who this book is for

The intended reader is a graduate student or early-career biostatistician who has working R fluency and is now learning to operate professionally: working on teams, satisfying regulatory requirements, producing reproducible artefacts that survive peer review.

The book assumes:

  • R proficiency at the level of R for Data Science (Wickham and Grolemund).
  • Familiarity with statistical methods at the master’s level (linear and generalised linear models, basic survival, regression diagnostics).
  • Comfort with the command line and basic file- system operations.

The book does not assume:

  • Prior Git, Docker, or renv experience.
  • Prior collaboration with clinical investigators.
  • Experience with regulatory or industry-sponsored research.

1.5 How to read this book

For each chapter, the recommended workflow is:

  1. Read the chapter through once, without running code. Notice the structure: an opening quiz, learning objectives, the statistician’s-contribution section, the technical sections, the LLM callouts, the exercises.
  2. Replicate the examples in your own environment. Type, do not copy-paste. The typing forces you to read each character; the copy-paste does not.
  3. Do the exercises without consulting an LLM. The cognitive cost of producing the answer yourself is part of the learning.
  4. Extend by pasting the AI-assisted practice prompts (the ‘Collaborating with an LLM’ callouts) into an LLM and critiquing the responses against your own work.

The book is dense. Reading two chapters per week is a sustainable pace; covering it in a quarter or trimester is realistic. The exercises are the work; the chapters are the scaffolding.

1.6 The chapter pattern

Every chapter follows the same structure:

  • Prerequisites quiz. Three open-ended questions, in the style of Advanced R (Wickham, 2019). Answer them honestly; if all three are easy, you can bypass the chapter. Answers appear in the Prerequisites-answers section at the end.
  • Learning objectives. What you will be able to do.
  • Orientation. A short prose framing of the topic.
  • The statistician’s contribution. What no tool can automate. The judgements that distinguish defensible practice from rote use.
  • Technical sections. The how-to.
  • Worked examples. Code that demonstrates the chapter’s tools on a realistic problem.
  • Collaborating with an LLM. Prompts that work, what to watch for, how to verify.
  • Principle in use. Three habits the chapter has been building toward.
  • Exercises. The work.
  • Further reading. Where to go next on this topic.
  • Prerequisites answers. End of chapter.

The pattern is repeated deliberately. By the third chapter you know where to find each component.

1.7 On the Age-of-AI framing

The companion textbook is titled Statistical Computing in the Age of AI. This Practicum is named for its content (Biostatistics Practicum) rather than the AI framing, but the framing is present throughout: every chapter has a ‘Collaborating with an LLM’ section showing how to use AI assistance for the chapter’s tools, and chapter 21 is the meta-chapter on AI-assisted coding more generally.

The position the books take is consistent: large language models are an amplifier of analyst productivity, not a replacement for analyst judgement. The book argues this position by demonstration. Use the AI assistance the book describes; verify it the way the book describes; form your own view.

1.8 What this book is, in three sentences

A graduate practicum in the daily craft of biostatistical data analysis. Six arcs: reproducibility, workstation, infrastructure, reporting, analysis practice, case studies. The test of the book is whether, after working through it, you can produce a reproducible, defensible analysis on a real biomedical question with an interdisciplinary team, using AI assistance without being misled by it.

Good luck.