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Worked solutions to the two Practice on your own problems from Chapter 6 (§ Practice). Work each problem yourself before opening the reveal — the moment of comparing your read to the worked answer is where the discipline gets internalised.
Problem 1 — the red-green status chart
Restated: A support-ops dashboard uses red / yellow / green cells to mark SLA status across 40 service categories. Name the specific failure this encoding creates for the ~8% of the audience with red-green colour vision deficiency, and rewrite the encoding so the meaning survives without losing the colour-coded scan.
Show worked solution
The failure. Under deuteranopia (the most common red-green CVD), red and green cells collapse to similar olive-brown tones and become almost indistinguishable; yellow reads as a slightly lighter olive against the same background. For roughly 8% of male viewers (and 0.5% of female viewers), a green-vs-red cell distinction disappears entirely and the dashboard reports one undifferentiated status across all 40 categories. Because the analyst’s own vision usually is not affected, this failure ships silently — the analyst inspects the chart, sees red-vs-green clearly, and cannot see what the audience will not see.
The rewrite. Add redundant encoding so the meaning is carried by at least two channels, one of which is not colour:
- Position or shape as the primary carrier: sort the 40 categories by status (all failing categories in one contiguous block, all warning in the next, all passing in the third), separated by a thin divider row; the reader scans the divider positions to see the counts without needing to decode any colour.
- Direct glyphs in each cell as a redundant carrier: a white check for pass, a hollow circle for warning, a cross for fail, sized to be legible at dashboard resolution.
- Colour retained as the tertiary carrier: keep the red/yellow/green (or better, red/amber/blue — blue survives deuteranopia intact), but treat it as reinforcement, not as the load-bearing signal.
The resulting dashboard passes an at-a-glance colour scan (the discipline of the «40 categories at once» view), passes the CVD simulator, and passes a black-and-white print. All three surfaces matter for a support-ops dashboard shipped to a mixed audience on unpredictable rendering hardware.
Meta-observation. The rewrite is not «stop using red and green.» The rewrite is «stop making red-vs-green the only difference between good and bad.» Chapter 6’s accessibility rule is about redundant encoding, not colour avoidance — the fix is additive.
Problem 2 — the heatmap with divergent data
Restated: A finance chart uses a sequential (light-to-dark blue) palette to show revenue variance from plan, where the data ranges from −30% to +40%. Name the palette-type mismatch and the one-sentence Refine direction that fixes it. Include which colour at zero.
Show worked solution
The palette-type mismatch. The data is divergent: it has a meaningful midpoint at zero (on-plan) with values that mean qualitatively opposite things on either side (positive variance = beating plan; negative variance = missing plan). A sequential palette from light-to-dark blue treats the range as a single ordered scale, so the eye reads +5% and −5% as adjacent shades of pale blue and cannot distinguish «just above plan» from «just below plan.» The pale end of the palette maps to whichever extreme happens to be smallest in absolute value, obscuring the sign entirely. This is the «wrong palette type for the data’s shape» failure Chapter 6 warns against.
The one-sentence Refine direction.
«Switch this heatmap to a diverging palette anchored at zero — use ColorBrewer RdBu reversed so that below-plan cells render red, on-plan cells render neutral white or light gray, and above-plan cells render blue; the palette must be perceptually symmetric around the midpoint even though the data range is asymmetric (−30% to +40%).»
Colour at zero: neutral white or the lightest gray in the palette. The midpoint must be visually distinct from both hues at low intensity so the reader can locate the on-plan cells at a glance. Red at zero would imply mildly negative; blue at zero would imply mildly positive; neutral is the only colour that carries the meaning «on plan.» Also: because the data range is asymmetric (extends further above zero than below), the analyst should lock the palette midpoint at zero rather than at the midpoint of the data range; otherwise a modest below-plan cell will render darker red than a much larger above-plan cell renders blue, and the visual weight will misrepresent the sign.
Meta-observation. The palette-type decision is upstream of the specific palette. «Which shade of blue?» is the wrong question when the data has a midpoint; «which two-hue diverging palette anchored at zero?» is the right question. Palette-type-to-data-shape is the mapping analysts most consistently get wrong when they accept Copilot’s default without naming the data’s structure in the prompt.
Related on this site
- Chapter 6 companion page — Aisha’s AIRS-cohort walkthrough, further reading, self-check quiz, and the hands-on accessibility-audit activity.
- Aisha’s AIRS cohorts dataset — the six-cohort scenario behind the two figures on the Chapter 6 companion page.
- All chapter companion pages.
- Errata — publication log for updates and corrections across all chapters.
About the three-tier Practice format
Each chapter of the book closes with a three-tier Practice block adapted from Cole Nussbaumer Knaflic’s course-adoption pattern in Storytelling with Data: Let’s Practice!: Practice with us (one worked problem with full solution in the book), Practice on your own (open problems whose solutions live here), and Practice at work (an open-ended prompt to apply the chapter’s move to a live artifact). This page hosts the middle tier.