Quiz — Chapter 27: Writing About Data

Q: The single most important structural move in writing about data is: Including more charts so the reader can see the evidence Reversing the order you did the work—leading with the finding, demoting the method Using precise statistical language so the result sounds rigorous Putting the methodology first so the reader trusts the finding

B. You did the work method → finding; the decision-maker needs finding → method. Reversing that order is the core move (§27.1, §27.2). (D) is the exact failure the chapter corrects; (A) and (C) don't address the ordering problem and often make it worse.

Q: A finding "is not a conclusion until it answers 'so what?'" means: You should always include a p-value Every finding must be stated with its confidence interval An analysis that stops at the number has stopped one sentence short of the recommendation the reader can act on You must run multiple analyses to confirm a finding

C. The threshold concept: the number is an observation; the conclusion is what the reader should do about it (§27.4, §27.5). The "so what?" test pushes a finding from observation through interpretation to recommendation.

Q: You should lead with the recommendation (rather than the finding) when: The reader's job is to act—approve, choose, or ship The reader is a peer who will audit your model The data is only correlational You're unsure whether the finding is real

A. Recommendation-first serves a reader whose output is a decision (§27.4). (B) calls for findings-first—a peer judges for themselves and would resent a pushed conclusion. (C) and (D) are about confidence, not ordering, and are handled in the caveats, not by hiding the finding.

Q: The three levels a finding moves through, in order, are: Method → results → discussion Observation → interpretation → recommendation Hypothesis → test → conclusion Data → chart → caption

B. Observation (what the data says) → interpretation (what it means) → recommendation (what to do). Most writing stops at observation; the "so what?" test pushes to recommendation (§27.5).

Q: A dashboard chart titled "Monthly Active Users" is weak because: The title is too long It names the *topic*, making the viewer decode the chart to find the point, instead of stating the takeaway or the question Dashboards shouldn't have titles "Monthly" should be abbreviated

B. A topic title is a label ([Chapter 9](../../part-02-building-blocks/chapter-09-visuals-and-data/index.md)'s Level 1). Better: state the takeaway ("Active users flat for three months") or, for changing data, the question it answers ("Are we retaining users?") (§27.7).

Q: On a *live* dashboard whose data changes hourly, a title like "Churn under control" is dangerous because: It's too informal It becomes false the moment churn rises, and the fixed words won't update to reflect it Viewers prefer raw numbers It should be in the tooltip instead

B. A fixed claim on changing data lies when the data moves. Title for the question ("Is churn under control?") or use a status indicator that updates (§27.7 Warning). The interpretation must stay true as numbers change.

Q: "Precision theater" refers to: Using too many charts Reporting more decimal places or exact figures than the analysis can actually support ("$1,243,887" from a rough model) Presenting findings in a live demo Hedging every statement

B. False precision signals you don't understand your own uncertainty. Round to what the analysis supports ("roughly $1.2M"); the honest round number beats the dishonest exact one (§27.9, Mistake 5).

Q: The honest way to handle a recommendation the data *partly* supports (it shows *what* but not *why*) is: State it confidently anyway—the reader wants a clear answer Omit the recommendation entirely Make the recommendation and flag the gap ("the likely cause is X, which we'd confirm before committing") Bury the recommendation in the method section

C. The "so what?" test demands an action but doesn't license one the evidence can't carry. Name the action and the gap honestly (§27.5, §27.9 Mistake 2). Clear writing makes a weak inference more persuasive, so overclaiming is especially dangerous.

Q: When an analysis must serve both a VP (needs the recommendation) and the analytics team (needs full method), the best first move is: Write one document pitched between them Layer one document—recommendation-first summary up top, full method in an appendix Send only the technical version; the VP can ask questions Send only the summary; the analysts can find the notebook

B. Layer first ([Chapter 2](../../part-01-writing-is-thinking/chapter-02-audience/index.md)'s lesson): a standalone summary for the decider, full method below for the auditors. If the needs truly conflict, write two documents. One compromised document serves neither (§27.9, Mistake 4).

Q: A dashboard label reading `Conversion: 2.5%` is improved most by: Making the font larger Adding a comparison and direction: `Conversion: 2.5% ▲ 0.4pt vs. last month · target 3.0%` Converting it to a pie chart Moving it to a tooltip

B. A bare number is an observation; a number with a comparison ("vs. what?") is an interpretation the viewer reads at a glance (§27.7). Every number on a dashboard should answer "compared to what?"

DataField.Dev

Quiz — Chapter 27: Writing About Data

Target: 70%+ before moving on.

Section 1 — Multiple Choice

1. The single most important structural move in writing about data is:

A) Including more charts so the reader can see the evidence
B) Reversing the order you did the work—leading with the finding, demoting the method
C) Using precise statistical language so the result sounds rigorous
D) Putting the methodology first so the reader trusts the finding

Answer

**B.** You did the work method → finding; the decision-maker needs finding → method. Reversing that order is the core move (§27.1, §27.2). (D) is the exact failure the chapter corrects; (A) and (C) don't address the ordering problem and often make it worse.

2. A finding "is not a conclusion until it answers 'so what?'" means:

A) You should always include a p-value
B) Every finding must be stated with its confidence interval
C) An analysis that stops at the number has stopped one sentence short of the recommendation the reader can act on
D) You must run multiple analyses to confirm a finding

Answer

**C.** The threshold concept: the number is an observation; the conclusion is what the reader should *do* about it (§27.4, §27.5). The "so what?" test pushes a finding from observation through interpretation to recommendation.

3. You should lead with the recommendation (rather than the finding) when:

A) The reader's job is to act—approve, choose, or ship
B) The reader is a peer who will audit your model
C) The data is only correlational
D) You're unsure whether the finding is real

Answer

**A.** Recommendation-first serves a reader whose output is a *decision* (§27.4). (B) calls for findings-first—a peer judges for themselves and would resent a pushed conclusion. (C) and (D) are about confidence, not ordering, and are handled in the caveats, not by hiding the finding.

4. The three levels a finding moves through, in order, are:

A) Method → results → discussion
B) Observation → interpretation → recommendation
C) Hypothesis → test → conclusion
D) Data → chart → caption

Answer

**B.** Observation (what the data says) → interpretation (what it means) → recommendation (what to do). Most writing stops at observation; the "so what?" test pushes to recommendation (§27.5).

5. The rule specific to an executive summary for a data analysis is:

A) Keep it under one page
B) Always include a methodology section
C) Lead with the insight, not the methodology
D) Use bullet points instead of paragraphs

Answer

**C.** The most common failure is opening with how the analysis was conducted; the summary must lead with the finding, implication, and recommendation, and only point to the method (§27.6). The standalone test from [Chapter 20](../../part-04-professional-workplace-writing/chapter-20-proposals-business-cases/index.md) still governs: could the reader decide from the summary alone?

6. A dashboard chart titled "Monthly Active Users" is weak because:

A) The title is too long
B) It names the topic, making the viewer decode the chart to find the point, instead of stating the takeaway or the question
C) Dashboards shouldn't have titles
D) "Monthly" should be abbreviated

Answer

**B.** A topic title is a label ([Chapter 9](../../part-02-building-blocks/chapter-09-visuals-and-data/index.md)'s Level 1). Better: state the takeaway ("Active users flat for three months") or, for changing data, the question it answers ("Are we retaining users?") (§27.7).

7. On a live dashboard whose data changes hourly, a title like "Churn under control" is dangerous because:

A) It's too informal
B) It becomes false the moment churn rises, and the fixed words won't update to reflect it
C) Viewers prefer raw numbers
D) It should be in the tooltip instead

Answer

**B.** A fixed claim on changing data lies when the data moves. Title for the *question* ("Is churn under control?") or use a status indicator that updates (§27.7 Warning). The interpretation must stay true as numbers change.

8. "Precision theater" refers to:

A) Using too many charts
B) Reporting more decimal places or exact figures than the analysis can actually support ("$1,243,887" from a rough model)
C) Presenting findings in a live demo
D) Hedging every statement

Answer

**B.** False precision signals you don't understand your own uncertainty. Round to what the analysis supports ("roughly $1.2M"); the honest round number beats the dishonest exact one (§27.9, Mistake 5).

9. The honest way to handle a recommendation the data partly supports (it shows what but not why) is:

A) State it confidently anyway—the reader wants a clear answer
B) Omit the recommendation entirely
C) Make the recommendation and flag the gap ("the likely cause is X, which we'd confirm before committing")
D) Bury the recommendation in the method section

Answer

**C.** The "so what?" test demands an action but doesn't license one the evidence can't carry. Name the action and the gap honestly (§27.5, §27.9 Mistake 2). Clear writing makes a weak inference *more* persuasive, so overclaiming is especially dangerous.

10. When an analysis must serve both a VP (needs the recommendation) and the analytics team (needs full method), the best first move is:

A) Write one document pitched between them
B) Layer one document—recommendation-first summary up top, full method in an appendix
C) Send only the technical version; the VP can ask questions
D) Send only the summary; the analysts can find the notebook

Answer

**B.** Layer first ([Chapter 2](../../part-01-writing-is-thinking/chapter-02-audience/index.md)'s lesson): a standalone summary for the decider, full method below for the auditors. If the needs truly conflict, write two documents. One compromised document serves neither (§27.9, Mistake 4).

11. A dashboard label reading Conversion: 2.5% is improved most by:

A) Making the font larger
B) Adding a comparison and direction: Conversion: 2.5% ▲ 0.4pt vs. last month · target 3.0%
C) Converting it to a pie chart
D) Moving it to a tooltip

Answer

**B.** A bare number is an observation; a number with a comparison ("vs. what?") is an interpretation the viewer reads at a glance (§27.7). Every number on a dashboard should answer "compared to what?"

12. In Dana's three memo versions, what makes Version 3 (recommendation-first) beat Version 2 (findings-first) for Renée specifically?

A) It's shorter
B) Renée's job is to act (move budget), so leading with the recommendation hands her the decision in line one; findings-first makes her assemble the action herself
C) It contains a different finding
D) It includes more methodology

Answer

**B.** Both contain the identical finding; the difference is what Renée must *produce*. Her output is a decision, so the recommendation is what she's reaching for. Findings-first would be correct for a reader whose job is to understand and judge (§27.4).

Section 2 — True/False with Justification

T1. "Leading with the finding instead of the method means hiding your work." True or False? Justify.

Answer

**False.** The method isn't hidden—it's *demoted* to where the reader who wants it can find it (below the finding, in an appendix, or a linked notebook). Leading with the finding prioritizes the reader; the honest move is to make the finding easy to reach *and* the method easy to audit (§27.9, Mistake 1).

T2. "A dashboard can deliver a full recommendation the way a memo can." True or False? Justify.

Answer

**False.** A dashboard is glanced at repeatedly on changing data, so it can't bake in a fixed conclusion—but it must still *interpret* through titles, labels, and tooltips that tell the viewer what a number means and whether it's good or bad. The memo, read once by a known reader, can carry the full recommendation (§27.7, FAQ).

T3. "Findings-first is always better than recommendation-first." True or False? Justify.

Answer

**False.** Recommendation-first is better when the reader's job is to *act*; findings-first is better when their job is to *understand and judge for themselves* (a peer, a regulator). Both beat method-first. The order follows the reader's purpose (§27.4, §27.9).

T4. "Including caveats weakens a data memo by making you sound unsure." True or False? Justify.

Answer

**False.** Caveats stated honestly (in a "how confident" line, not the lead) build trust and protect you from the embarrassing follow-up question. A finding presented without its limits is one that fails when someone asks the obvious question. Honesty about confidence is part of the craft, not a weakness (§27.9, Mistake 3).

T5. "An interpretive caption matters more in a data report than in a novel because reports are scanned, not read linearly." True or False? Justify.

Answer

**True.** Reports are raided for conclusions; figures are the first thing a scanner's eye lands on, so the caption is often the only text attached to your central evidence that a hurried reader processes. A label-only caption leaves the scanner with a topic and no finding (§27.8; ties [Chapter 4](../../part-01-writing-is-thinking/chapter-04-structure/index.md) and [Chapter 9](../../part-02-building-blocks/chapter-09-visuals-and-data/index.md)).

T6. "If you can quantify a finding in dollars, you've usually turned an interpretation into an actionable recommendation." True or False? Justify.

Answer

**Mostly True.** Translating a finding into the reader's currency (dollars, customers, risk) is often what turns a Level-2 interpretation into a Level-3 recommendation worth approving—it makes the stakes concrete and the action obvious (§27.5). The caveat: the dollar figure must be supportable and honestly rounded, not precision theater, and the recommendation it implies must be one the data can carry.

Section 3 — Short Answer

S1. In one sentence, state the "so what?" test and what it's for.

Model answer

The "so what?" test is the habit of asking, of every number you report, "what should the reader do about this?"—and not stopping until you've pushed the finding from observation to a recommendation the reader can act on. *(Rubric: names the question + the goal of reaching an action.)*

S2. A memo to a VP currently opens with three sentences of methodology. State the two changes you'd make and why.

Model answer

(1) Move the finding/recommendation to the top, in the VP's units, so they can act after the first paragraph; (2) compress the method to a single "how confident" line and demote it below the finding. Why: the VP's job is to decide, not to audit, so the method is in the way up top and reassuring down below. *(Rubric: reorder + demote/compress, justified by the reader's purpose.)*

S3. Name the three places dashboard text does most of its work, and the one-line job of each.

Model answer

**Titles**—state the takeaway or the question the chart answers, not the topic. **Labels**—name things in the viewer's language with units and a comparison ("vs. target"). **Tooltips**—add the interpretation, definition, or caveat that would clutter the chart if always visible. *(Rubric: all three named with a correct one-line job.)*

S4. Why does leading with method subtract credibility with a non-technical decision-maker, when it adds credibility with a peer?

Model answer

A peer reads method as precision and evidence—it's how they verify the result, so it builds trust. A non-technical decision-maker can't evaluate the method and doesn't want to; leading with it signals "this isn't written for you" (the curse of knowledge from [Chapter 2](../../part-01-writing-is-thinking/chapter-02-audience/index.md)), spends their patience before the answer, and makes them feel the writer misjudged their needs. Same content, opposite effect, because the readers do different things with it. *(Rubric: ties the difference to what each reader does with the method.)*

S5. Give the standalone test for a data report's executive summary, and one thing that fails it.

Model answer

The test: could a reader who reads *only the summary* make the decision? It fails when the summary leads with methodology (or ends its opening paragraph promising findings instead of stating them)—the reader learns a study happened but not what it found or what to do (§27.6; [Chapter 20](../../part-04-professional-workplace-writing/chapter-20-proposals-business-cases/index.md)). *(Rubric: states the test + one concrete failure mode.)*

Section 4 — Applied Scenario

AS1. (Rebuild a memo.) Here is a method-first memo. Rewrite it as a recommendation-first memo to a non-technical VP of Sales. Keep the finding; demote the method to one line; supply the missing recommendation and a next step.

Subject: Lead-scoring analysis I analyzed 14 months of CRM data (about 22,000 leads) and built a gradient-boosted model to predict which leads convert. The model reached an AUC of 0.78. The strongest predictor was whether a lead attended a product webinar before the first sales call—webinar attendees converted at 34% versus 11% for non-attendees, controlling for company size and source. Charts attached; let me know if you'd like to discuss.

Rubric

A strong rebuild: - **Subject line** states the recommendation or finding ("Recommend we route every lead through a webinar before the first call"), not "Lead-scoring analysis." - **First two lines** give the recommendation and a next step ("…I'd like to pilot a webinar-first sequence for one region this quarter"). - **Finding as support**, in the reader's units: webinar attendees convert at 34% vs. 11%—**3× higher**—holding after company size and source. - **One "how confident" line**: based on 14 months of CRM data and a predictive model; the gap is large and holds after controls. (No "AUC 0.78" in the lead.) - **Honest hedge** if claiming causation: attendance *correlates* with conversion strongly, but a pilot confirms whether the webinar *causes* it (engaged leads may both attend and convert). - Method ("gradient-boosted model, AUC 0.78") demoted to a "details below" line, not deleted. Score: 4 = all of the above; 3 = leads with finding but no recommendation/next step, or method still prominent; 2 = reordered partially; 1 = still method-first.

AS2. (Interpret a chart.) You have a chart showing weekly new signups rising steadily for ten weeks, then dropping sharply in week 11 (which coincides with a pricing change). Write (a) a Level-3 interpretive caption and (b) one chart annotation with its alt-text.

Rubric

(a) Caption states what it *means* and gestures at action: e.g., "New signups grew steadily for ten weeks, then fell 40% the week we raised prices (Figure X)—suggesting the new pricing is suppressing top-of-funnel demand and warranting a review." (b) Annotation sits at week 11 with a few words ("Pricing change → signups drop") and the alt-text describes the pattern for a non-visual reader ("Signups rise for ten weeks, then drop sharply in week 11, coinciding with the pricing change"). Note: the caption should *associate* the drop with the pricing change without overclaiming causation if other factors could be involved—"coinciding with," "suggesting," not "caused by," unless the analysis isolated it.

Scoring & Next Steps

Score	What to do
< 50%	Re-read §27.1–§27.4 (the failure, the memo skeleton, and Dana's three versions). The ordering move is the whole chapter—make sure you can explain why method-first fails.
50–70%	Redo Part B (Revise This) in the exercises, focusing on demoting the method and pushing findings to Level 3.
70–85%	You've got it. Proceed to Chapter 28, and do the Project Checkpoint memo if you haven't.
> 85%	Try the Part E extensions—audit a real public dashboard, or write your finding in both structures and defend the choice.