Practice Workbook
Companion volume to The Defensible Decision
The printed workbook is now available. These chapter pages are its online complement — the same fourteen chapters, each with curated further reading, a self-check quiz, and a hands-on activity you can run against the teaching datasets.
Each chapter of The Defensible Decision has a companion page here. Every page follows the same shape: curated further reading beyond Appendix C (five external resources per chapter — real papers with correct DOIs, real books with correct editions, real practitioner sites), a five-question self-check quiz covering the chapter's Key terms, a hands-on activity against a teaching dataset or an artifact of your own, related site resources, and the chapter's operative citations from Appendix C.
Use the further reading to widen a discipline into its primary literature. Use the quiz to check whether concepts have moved from working memory into reflex. Use the activity to find where the evidence or decision logic still needs work.
Chapter 1 — Why Principles First, AI Later
The thesis chapter. Companion covers automation-bias research (Parasuraman & Manzey), the Cleveland–McGill perceptual paper, Munzner’s augment-vs-replace framing, Mollick, and Cairo. Activity: read a Copilot chart against the Part I principles.
Open Chapter 1 →Chapter 2 — Context Before Any Chart
The BRD discipline chapter. Companion covers Kozyrkov, Bryar & Carr, Amy Ko, Nielsen Norman on stakeholder interviews, and Julie Zhuo. Activity: complete the CloudRevenue starter BRD end-to-end.
Open Chapter 2 →Chapter 3 — Story: From Data to Argument
The Big-Idea and three-act-arc chapter. Companion covers Knaflic’s practice book, Duarte’s DataStory, Heath & Heath, Zinsser, and the FT Visual Vocabulary. Activity: write the Big Idea before the chart against M365Marketing.
Open Chapter 3 →Chapter 4 — Chart Selection
Matching the question to the form. Companion covers Cleveland’s Elements of Graphing Data, Munzner on marks and channels, the FT Visual Vocabulary, Datawrapper Academy, and Cairo. Activity: run family-before-type on five CloudRevenue questions.
Open Chapter 4 →Chapter 5 — Focus and Decluttering
The discipline of removal. Companion covers Tufte’s primary source, Ware’s perceptual textbook, Knaflic’s clutter chapter, Kosara at eagereyes.org, and the Bateman et al. embellishment study. Activity: remove-until-it-breaks on a Copilot dashboard.
Open Chapter 5 →Chapter 6 — Color, Type, and Accessibility
Visual rhetoric. Companion covers ColorBrewer, Ware, WCAG 2.1 contrast standards, the Coblis simulator, and Bringhurst on typography. Activity: run the CVD + grayscale + typographic-hierarchy audit on a Copilot dashboard.
Open Chapter 6 →Chapter 7 — From Hand-Built to AI-Directed
The transition chapter. Companion covers Mollick’s Co-Intelligence, the Dell’Acqua et al. jagged-frontier study, Kahneman, Kozyrkov on decision intelligence, and the Anthropic prompt-engineering guide. Activity: run the six-check readiness gate on yourself.
Open Chapter 7 →Chapter 8 — Dialog Engineering and the CSAR Loop
The CSAR loop chapter. Companion covers the Anthropic and OpenAI prompt guides, Simon Willison’s blog, the Mollick «Assigning AI» catalog, and The Verification Habit. Activity: walk one full CSAR loop end-to-end against CloudRevenue.
Open Chapter 8 →Chapter 9 — Directing Copilot Report Generation
Wrong-measure, wrong-grain, wrong-narrative. Companion covers Microsoft Learn Copilot docs, Ferrari & Russo’s Definitive Guide to DAX, SQLBI DAX Patterns, Kimball on dimensional modeling, and the Prep-for-AI documentation. Activity: reconcile a Copilot report against a DAX source of truth.
Open Chapter 9 →Chapter 10 — AI Visuals
Key Influencers, Anomaly, Decomposition Tree, Smart Narrative. Companion covers Microsoft Learn per-visual docs, Pearl’s Book of Why, and Datawrapper Academy on narrative titles. Activity: run all four per-visual verification moves on one dashboard.
Open Chapter 10 →Chapter 11 — Prep for AI and Data Questions
AI data schemas, verified answers, AI instructions. Companion covers Microsoft Learn per-component docs, plus Norman’s Design of Everyday Things on the confirmation-turn pattern. Activity: run Prep for AI on a sandbox semantic model.
Open Chapter 11 →Chapter 12 — Dashboard Design
Composition, hierarchy, the 5-Visual Rule. Companion covers Few’s Information Dashboard Design, Wexler et al., the Big Book of Dashboards blog, Kirk on page composition, and Nielsen Norman on visual hierarchy. Activity: run the full composition Refine on a Copilot dashboard.
Open Chapter 12 →Chapter 13 — When AI Misleads
Vibe charting, the five diagnostics, the four failure modes. Companion covers Parasuraman & Manzey, Kahneman’s Thinking, Fast and Slow, the Buçinca et al. forcing-functions study, Nickerson on confirmation bias, and Karpathy on vibe coding. Activity: score a shipped artifact on the five diagnostics.
Open Chapter 13 →Chapter 14 — Responsible AI in Business Analytics
Provenance, fairness, decision boundaries, disclosure. Companion covers Microsoft’s Responsible AI Standard, the NIST AI RMF, Barocas / Hardt / Narayanan on fairness in ML, Suresh & Guttag on lifecycle harm, and Kroll et al. on accountable algorithms. Activity: run the four-moves pass on an artifact you own.
Open Chapter 14 →Appendix E — BRD Field Guide
Start with the BRD template, compare completed exemplars, and use intentionally underspecified starters to practice turning a business question into an analytical contract.
Open the Field Guide →Appendix F — Statistics Claim Check
Check averages, group differences, associations, and AI-generated drivers before using a dashboard claim in a recommendation or decision. This companion includes worked checks and a formula-visible workbook.
Open the Claim Check →