Conventions

This page summarises the visual conventions used throughout the book.

Code

R code appears in syntax-highlighted blocks. Output is prefixed with #> to make the boundary between input and output explicit:

mean(c(1, 2, 3, 4, 5))
#> [1] 3

Inline code is in monospace. Function calls always include parentheses (mean() rather than mean) so that they are unambiguously functions. Package-qualified calls (dplyr::filter) appear when the function is not universally known, when there is name-collision risk, or when the chapter is teaching package usage.

Callouts

Three callout types appear:

TipTip

A small practical recommendation.

A short question testing comprehension of the just-read material. Click to expand the answer.

WarningWarning

A pitfall the reader may otherwise hit.

Cross-references

Within this book, sections, figures, and tables are referenced by their Quarto label (@sec-monte-carlo-human, @fig-mcmc-trace, @tbl-comparison). These resolve to clickable links in HTML and proper figure/table numbers in PDF.

References to the companion volumes Statistical Computing in the Age of AI and Biostatistics Practicum use prose pointers rather than Quarto cross-references, because cross-references do not resolve across separate books. For example: ‘see the Optimisation chapter of the companion Statistical Computing in the Age of AI volume’.

Mathematical notation

Conventional notation throughout. Vectors are bold lower-case (\(\mathbf{x}\)); matrices are bold upper-case (\(\mathbf{X}\)); scalars and parameters are non-bold. Estimators carry hats (\(\hat\theta\)). Sample size is \(n\); parameter dimension is \(p\).

Chapter structure

Every content chapter follows the same template:

  1. Learning objectives. What you will be able to do after reading.
  2. Orientation. A short prose framing.
  3. The researcher’s contribution. What no tool can automate. The judgements at the centre of the chapter.
  4. Content sections with Check-your-understanding callouts at natural pauses.
  5. Collaborating with an LLM on the chapter topic. Prompt / Watch for / Verification triples for AI assistance.
  6. Exercises. The work.
  7. Further reading. Where to go next on the topic.

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