Illowsky & Dean — Introductory Statistics 2e

Barbara Illowsky and Susan Dean, both of De Anza College; published by openstax (Rice University), ©2026, CC BY-NC-SA 4.0, 847 pages. The PDF held in raw/ was built 2026-04-20. Grown out of the authors’ earlier Collaborative Statistics.

T1 — the complete text, peer-reviewed and openly licensed, read directly.

What it is, in its own words

A one-semester introduction to statistics “geared toward students majoring in fields other than math or engineering.” It “assumes some knowledge of intermediate algebra and focuses on statistics application over theory.”

That sentence is why this book matters to this spoke, and it should be read as a declaration rather than an apology. Every other statistics text here derives; this one decides not to, on purpose, for a stated audience. See mathematical-statistics for where it sits against larsen-marx-mathematical-statistics.

The 13 chapters

Sampling and data · descriptive statistics · probability topics · discrete random variables (binomial, geometric, hypergeometric, Poisson) · continuous random variables (uniform, exponential) · the normal distribution · the central limit theorem · confidence intervals · hypothesis testing with one sample · with two samples · the chi-square distribution (goodness-of-fit, independence, homogeneity, single variance) · linear regression and correlation · the F distribution and one-way ANOVA.

The route is the standard American service course, and the order is an argument: sampling before description, description before probability, probability only as far as it takes to reach the central limit theorem, and then four chapters of inference. Nothing is proved on the way. The Student t, chi-square and F distributions arrive as facts with tables and calculator keystrokes attached.

An alternate sequencing is printed in the preface, moving regression (ch. 12) up to third, directly after descriptive statistics — with a warning that the chapters “were not written to be completely independent.”

The tool it assumes, and why that is the interesting part

The TI-83/83+/84/84+ graphing calculator. There is a “Technology Icon” marking where a calculator or software is recommended, step-by-step keystroke instructions inline, and a whole appendix (G) of calculator notes.

This spoke has been recording the computational tool each text assumes, and the range is now wide enough to be a finding in itself: carroll-geometric-function-theory assumes none, judson-abstract-algebra assumes Sage, and this one assumes a handheld calculator design that dates to the 1990s. The tool is not incidental to the pedagogy — a book that reaches the F distribution without calculus needs the student to obtain numbers from somewhere, and the calculator is what stands in for the theory that was left out.

Labs, projects, and a very large exercise apparatus

The statistics labs are the authors’ own contribution, carried over from Collaborative Statistics: end-of-chapter activities where students design, run and interpret an analysis, with learning outcomes, time-on-task estimates and sample data tables. There are data-collection and sampling experiments in chapter 1, playing-card and three-dice distribution experiments in chapter 4, lap-time and pinkie-length normal-distribution labs in chapter 6, pocket-change and cookie-recipe CLT labs in chapter 7, two chi-square labs, and an ANOVA lab.

Around them: Try It problems after examples, Collaborative Exercises, Practice, Homework, and “Bringing It Together” sets that mix chapters, plus appendices holding review exercises for chapters 3–13, four practice tests and final exams, data sets, group and partner projects, solution sheets, a formula/symbol glossary, the calculator notes, and tables.

Answers are deliberately uneven: examples answered in place, Try It answers on the student resources page, a selection of practice and homework answers at each chapter’s end, everything in the instructor guide — and no answers at all for collaborative exercises and labs, “due to the high variability in responses.”

What changed in the second edition

Stated plainly in the preface: “currency, relevance, accuracy, and belonging.” Dozens of examples and references updated for contemporary contexts, data refreshed, errata resolved, and “several hundred edits related to gender, race, ethnicity, age, academic status, and societal issues.”

The mathematics did not change. What changed is who the examples are about — and the revision is explicitly constrained by the installed base: problems “retain their original numbering, so that instructors and online homework providers can move to the new edition with minimal impact on their assignment structures.”

That constraint is worth noticing. A textbook edition here is being versioned the way a widely deployed piece of software is versioned, with backward compatibility for downstream integrations as a design requirement. No other text in this corpus reports such a pressure, and it is the clearest evidence the spoke has that a service-course textbook is an infrastructure product rather than an authored one.

mathematical-statistics · probability-theory · larsen-marx-mathematical-statistics · herzog-understanding-statistics · stirzaker-elementary-probability · openstax · barbara-illowsky · susan-dean · de-anza-college · mathematics · synthesis