← Field manual directory

Department of Reasonable Doubt

Document
FM–001
Revision
01
Updated
Status
Public preview

Reading charts and their claims

Read the chart. Then read around it.

Charts can make a real change look dramatic, small, simple, or certain. Before accepting the impression, identify what the chart measures—and what it leaves out.

Get the chart-reading checklist

Purpose Separate what a chart measures from what its presentation encourages you to believe. Includes fictional practice and three sourced historical examples; not a rating of publishers.

Pause, Trace, Check

  1. Pause. Notice the conclusion you feel invited to make.
  2. Trace. Find who made the chart and the underlying data.
  3. Check. Test the measure, scale, comparison, and uncertainty.
  4. Map incentives. Ask what the publisher wants, without assuming that answers the question.
  5. Calibrate. State only what the available evidence supports.

What is this chart actually saying?

A chart is an argument made with choices: which numbers to collect, where to start, what to compare, and what context to show. Those choices are not automatically dishonest. They are reasons to look more closely.

Average weekday bus rides increased from 95,000 to 105,000 A line rises across four months. The vertical axis begins at 90,000 rather than zero, making a ten-thousand-ride increase appear steep. Transit use surges after campaign launch Average weekday rides 105k100k95k90k AprilMayJuneJuly
Fictional teaching example: These invented figures do not describe an actual transit system or campaign. The headline suggests a large surge. The data shown rise from 95,000 to 105,000 average weekday rides—about 11%. The chart may be accurate, but its truncated vertical axis amplifies the visual slope.

First, separate the data from the headline

The plotted values support a limited claim: average weekday rides were higher in July than April. They do not alone show that a campaign caused the change, that total annual ridership surged, or that the change was unusually large.

Notice the leap

“Rides increased” describes the numbers. “The campaign increased rides” adds a causal explanation the chart does not establish. That small change in wording makes a much bigger claim.

A proportionate reading of the fictional chart: “Average weekday rides increased by roughly 11% from April to July. Four months without comparisons to other years or places do not establish that the campaign caused the increase or that it represents a long-term surge.”

Evidence file 01 / Clear presentation

Context has been authorized for release.

Our first exhibit is not a trap. NASA's global temperature chart lets you inspect the annual observations, the longer-term pattern, and the uncertainty. A suspicious amount of useful information has been left in view.

NASA GISTEMP chart from 1880 onward: black annual observations fluctuate around a red smoothed line, with gray uncertainty shading. Temperature anomalies rise over the long term. The vertical axis is degrees Celsius relative to the 1951–1980 average.
Original chart: NASA/GISS/GISTEMP v4. Snapshot retrieved September 13, 2026; the live source is updated over time. Open full-size chart · Source, explanation, and data downloads.

Read the reference point, not just the direction

The vertical axis shows a temperature anomaly: a difference from the 1951–1980 average. Zero means that reference average—not freezing, and not “no temperature.” A value of +1°C means one degree warmer than that baseline. It does not mean the global temperature is one degree.

The black annual points preserve year-to-year variation. The red LOWESS line summarizes the longer-term pattern instead of replacing the observations. The gray band shows the estimated 95% uncertainty interval for annual values; it is not a forecast range or the variation in local weather. NASA's source page explains the smoothing and uncertainty calculations and supplies the data.

Why we approve this particular paperwork

The long time span makes it harder to mistake a short dip for the entire story. Units, baseline, observations, trend, and uncertainty are all inspectable. Even so, the image needs its accompanying explanation: “LOWESS” and “LSAT+SST” are not household vocabulary. Good is not the same as beyond improvement.

Authorized conclusion: This dataset shows long-term warming with annual variation and measurement uncertainty. The chart alone does not establish the causes, forecast next year's temperature, or tell you what happened in every town. Those require additional evidence. Our approval applies to the presentation—not a lifetime exemption from scrutiny for its publisher.

Data reference: GISTEMP Team (2026), GISS Surface Temperature Analysis, version 4, accessed September 13, 2026; Lenssen et al. (2024), A GISTEMPv4 observational uncertainty ensemble. NASA does not endorse Suscorp.

Evidence file 02 / Acknowledged design error

The dogs did not change. The axes did.

The Economist's “Fit as a butcher's dog” chart compared average adult weight and neck size for dogs registered with the UK's Kennel Club. In the original, the two lines practically travel as a corporate delegation. In the redesign, the same data tell a less synchronized visual story.

The Economist's original and redesigned dog-size charts, 2006–2015. Originally, axes of 18–21 kilograms and 42–45 centimetres make the lines nearly overlap. The redesign changes the scales and the lines separate. Both measures decline in both versions.
Original and redesign: The Economist, reproduced for this critique. Sarah Leo's 2019 retrospective; image also reproduced by Robert Kosara. Original labels and footnotes retained. Open full-size comparison · Publisher's explanation · Independent discussion.

Two rulers. One suspiciously tidy picture.

The original gives 18–21 kilograms and 42–45 centimetres the same vertical space. Both spans cover three units, but a kilogram is not a centimetre, and those changes are not proportionally equivalent. Adjusting two independent scales can bring lines together or pull them apart without altering a single observation.

Both measures do fall. What the near-overlap invites you to overread is that they change almost identically. Changing an axis does not change a calculated statistical correlation; it changes the visual impression. Nor does a falling average prove individual dogs got smaller. A changing mix of registered breeds could affect the average.

Why call this an error, rather than a plot?

Because we have more than the image. In Mistakes, we've drawn a few, visual journalist Sarah Leo describes the publication's misleading charts as unintentional, identifies this scale problem, and offers a correction. That is the publisher's account, not a mind-reading certificate. “Acknowledged design mistake” is better supported than either “secret deception” or “proven pure intentions.”

Authorized conclusion: Both averages declined in the displayed period. Their original visual near-match deserves less confidence than it first appears to demand. The redesigned chart is better for this comparison—not a perfect chart or proof of causation.

Sources: Sarah Leo, The Economist, March 27, 2019; Robert Kosara, Critiquing and redesigning, April 18, 2019. The example concerns historical registered-dog averages, not a current assessment of dog health.

Evidence file 03 / Documented misleading persuasion

The conclusion arrived before the evidence.

A bad chart does not automatically prove deception. This case has evidence beyond the chart: an agreed statement of facts in a criminal proceeding concerning OxyContin marketing. The distinction matters more than our corporate commentary.

Historical misleading claim—not medical guidance: The training diagrams below were used to promote a false impression that OxyContin had less abuse potential than short-acting opioids. They are reproduced to examine that claim, not endorse it.

Historical training diagrams: the top labels short-acting opioid use inappropriate and shows repeated peaks labeled sedation or euphoria and troughs where pain returns. The bottom labels long-acting use appropriate and shows a flatter pain-relief line, asserting minimized sedation or euphoria. Neither vertical axis gives numeric concentrations. These misleading claims are examined in the surrounding text.
Historical training exhibit, not measured clinical results. Diagram-only crop from the agreed statement of facts, paragraph 25, page 8. The scan's original wording and image quality are retained. Open full-size exhibit · Full court record, pages 8–9 (PDF).

A smooth line is not a safety study

The top diagram shows repeated rises into a zone labeled “Sedation, euphoria” and falls where pain returns. The bottom stays mostly within a “Pain relief” band. Before you inspect anything else, the titles have already called one use “inappropriate” and the other “appropriate.” The vertical axes provide no numerical blood concentrations.

A schematic can legitimately explain an idea. It cannot stand in for a measured comparison and then establish lower abuse potential. Here there are two separate leaps: this drawing represents how the drugs compare, and this shape establishes a safety advantage. Both need evidence. A pleasant curve is not a substitute.

What the record actually establishes

Paragraph 25 says that, during a December 1998 sales-manager training session sponsored by Purdue, a retained pharmacist used these diagrams instead of the actual clinical-data comparison and falsely stated that OxyContin's fewer peak-and-trough effects meant less euphoria and less abuse potential.

Paragraph 26 describes some new sales representatives being permitted in training to draw their own blood-level graphs to falsely represent those claims. Paragraph 27 records certain representatives using similar graphics and false claims with health-care providers between 1999 and June 30, 2001.

That documented use supports calling this misleading persuasion, rather than guessing motives from the line's appearance. It does not establish what every employee knew or an individual designer's private intentions. We do not need to enlarge the finding to make the lesson serious.

Authorized conclusion: These diagrams were used to sell a false impression of lower abuse potential, as documented in the company's agreed statement of facts. That conclusion rests on the record, not just on our dislike of the design. This historical analysis is not advice about an individual's treatment.

Primary source: United States v. The Purdue Frederick Co., Inc., No. 1:07-cr-00029-JPJ, Document 5-2, filed May 10, 2007, Attachment B, paragraphs 21–27. The exhibit is reproduced only for analysis and criticism; its promotional claims are not adopted by Suscorp.

Seven questions for any chart

1. What is being measured?

Read the title, axis labels, unit, and definitions. Here it is average weekday rides—not revenue, unique riders, or all trips.

2. Who made it, and from what data?

Find the publisher, dataset, collection method, and date. A chart without a source is a starting point for research, not a final answer.

3. What period is shown?

Four months can conceal seasonal patterns, long-term trends, or an unusual starting point. Ask to see a longer time series.

4. What is the baseline?

Check where the axis starts. A line chart need not start at zero, but a truncated axis should change how strongly you react to the slope.

5. What comparison is missing?

Compare with prior years, similar places, population changes, service changes, or an appropriate control group when relevant.

6. Is the conclusion stronger than the chart?

Association is not causation. A rise after an event may have several explanations.

7. What uncertainty is hidden?

Look for sample size, ranges, error bars, revisions, missing responses, and whether small differences are meaningful.

Missing information is not proof of deception

Every chart simplifies. Space, audience, and data availability require choices. The useful question is not “Is this chart biased?” but “How does this presentation limit what I can responsibly conclude?” Look for the underlying source before assigning intent.

Your suspicion must also submit evidence.

Separate three questions: What do the numbers support? What does the design encourage me to believe? What evidence explains the creator's conduct? A clear chart earns a bounded conclusion. An error earns a correction. An accusation of deception needs more than a suspicious-looking axis.

Real-example briefing added September 13, 2026. Source images retain their original colors in both themes; full-size links are provided for small labels. Commentary is Suscorp's, not the source publishers'. Independent editorial review remains outstanding for this public preview.

Keep the evidence attached.

Full-size images, attribution, context, and specific references remain beside each exhibit. The transit figures are invented; none of these sources is evidence of a real transit campaign.

Revision is an expected outcome.

· Revision 01: introduced the standard manual format, monochrome interface, and consolidated source index. Historical source-image bytes, claims, and their limitations retained.

Revision numbering begins with this format edition. The fictional lesson predates it; real examples were added September 13, 2026. Independent editorial review remains outstanding. Department names are fictional editorial identities, not claims of staffing or review.

Return to document header ↑ · Method and institutional disclosures