Will These 6 Sneaky Charts, Maps, and Graphs Deceive Your Students?

In 1863, the photographer Alexander Gardner and his assistant Timothy O’Sullivan snapped Home of a Rebel Sharpshooter, encapsulating the brutality of the Battle of Gettysburg for a war-weary country, then two years deep into one of the bloodiest conflicts in American history. Over a century later, scholars poring through Civil War archives determined that the photo—of a single, dead Confederate soldier who apparently perished alone and in agony—had been staged to intensify its emotional impact.

Gardner was a Union loyalist. Perhaps the underlying reality the photo captured was true; the presentation, though, was engineered to tell his side of the story.

Other faked wartime photographs, from Robert Capa’s stunning picture of a soldier collapsing with a mortal wound during the Spanish Civil War to Fred Morley’s improbable image of an English man calmly delivering milk during the Blitz in World War II, have not shaken the photograph’s claim to objectivity. The still image remains an important tool for journalists, carrying with it the implicit promise of accuracy as an impartial and contemporaneous witness to events. Modern readers continue to assume that “pictures don’t lie—you can believe what you see,” says Santiago Lyon, head of Advocacy and Education for the Content Authenticity Initiative. “But of course pictures can lie, and they do lie.”

They aren’t the only medium susceptible to manipulation. Dressed in the authority of statistical and mathematical certainties, data visualizations like charts, maps, and graphs appear to offer an unfiltered view of measurable phenomena like economic conditions, weather, or job markets. “Numbers are very persuasive, and so are charts, because we associate them with science and reason,” writes Alberto Cairo, Knight Chair in Visual Journalism at the University of Miami, in his book How Charts Lie. But as Cairo’s book title suggests, numbers too can be made to lie.

Over the past 20 years, the use of data visualizations has exploded in fields like politics, science, finance, and public health. This, Cairo says, is in response to the increasing availability of data, the development of more tools to “obtain, analyze, and visualize it,” and the public’s growing appetite for information that appears to explain complex cultural issues—or simply aligns with their convictions.

Inside newsrooms around the world, data visualizations are often as important to the storytelling as the written words they accompany. Sarah Leo, a visual data journalist at The Economist, whose award-winning data team produces around 40 charts a week, is deliberate and methodical about fairness in her work. “You obviously want to make sure you’re presenting things in a way that is not just hammering your point,” she told Edutopia in a recent interview. “The chart needs to be fair. You can’t just exaggerate or cherry-pick things.”

Not everyone is so careful. Like Gardner’s flagrantly staged tableau, provocateurs on social media lay out misleading data but work subtler angles—truncating axes, mislabeling charts, or distorting scale to create the impression of objectivity. As dubious pie charts and doctored electoral maps increasingly sit alongside well-designed data visualizations on platforms like Facebook or X—provoking both outrage and insight, though not in equal measure—the need for greater data literacy becomes paramount, Cairo says.

Debates in the modern era “are driven by statistics, and by charts,” he explains. “To participate in those discussions as informed citizens, we must know how to decode—and use—them.”

Snap Judgments

The antidote, Brookline Public Schools’ K–8 math specialist Jenna Laib says, is deliberate practice and plenty of opportunities to “develop strong data habits” like “slowing down, thinking deeply, and constructing meaning.” With time, Cairo writes, students will stop seeing charts “as if they were merely illustrations,” and actually start reading and interrogating them.

Without help, first impressions tend to stick. In a 2026 study, researchers asked college students to interpret various charts while explaining their thinking out loud. Even the sharpest students were fooled by subtle design choices that nudged them toward the wrong conclusions.

When presented with a chart—misleading or not—inexperienced students made intuitive judgments “rather effortlessly,” by quickly eyeballing bar heights or line slopes without closely examining elements like labels, visual or mathematical scale, or the credibility of the claim, the researchers found. This led to a “preliminary conclusion” that quickly hardened into conviction, making the first impression of charts and graphs hard to shake. Even when students were asked to read labels and check scales carefully, “the damage done in the first phase was very unlikely to become undone in the second phase.”

Students with higher levels of data literacy, the researchers found, were better able to move beyond a chart’s surface features and engage with the author’s visual design choices. “In this phase, readers go beyond the imaging and start reading titles, subtitles, category names, legends, and numbers,” the researchers explain. “At this point, readers can distinguish actual data points and compare them to each other.”

To showcase the many ways that charts and graphs can be used to influence (and even mislead) the public, we’ve curated a collection of data visualizations that demonstrate common ways data can be distorted. Each set of charts includes the original version, notes on the data sources, simplified and cleaned-up data you can download and use to replot the charts, and several new versions of the charts that treat the data with more fidelity. To get you started on planning classroom activities, we’ve also created a student worksheet that can be used to reflect on the data distortions (see inset) and dropped a few targeted classroom activities alongside each chart.

Marketing 101: Who Says?

At first glance, the superior truck is clear: Chevy appears to be far more reliable than its competitors, with major leads against Ford and Toyota while dwarfing Nissan. But small differences can be exaggerated by a simple tweak of the vertical axis.

The towering blue “Chevy” bar—featuring a larger and more prominent numerical label—makes an otherwise small difference appear like a commanding lead. Since the y-axis begins at 95% instead of zero, gaps appear proportionally larger. In reality, however, all four models are clustered close together, with roughly a three-percentage-point difference between the top and bottom performers. Plot the chart with the vertical axis starting at zero, and the differences seem trivial.

“This is one of the most common ways graphs misrepresent data, by distorting the scale,” explains educator Lea Gaslowitz in a TED-Ed video. By zooming in on a small portion of the y-axis, chart makers can manipulate minor differences in values, turning a molehill into a mountain.

Other troubling elements of the Chevy chart: There is an asterisk at the top of the chart with no corresponding footnote, and the language of the chart’s claim is vague—phrases like “still on the road” and “most dependable” require further clarification. Other models may be more reliable, for example, if a different benchmark is used, like the frequency of repairs or five-year maintenance costs. Brands choose data that puts them in a favorable light, even if other measures would tell a different story.

Data notes: There is no data source listed in the Chevy advertisement, making it difficult to verify the figures or determine which year the data represents. It’s possible that the data was internally collected by Chevy and there’s no public report to review. Independent groups such as U.S. News & World Report and Consumer Reports conduct their own analyses of truck reliability, using different measures that may lead to different conclusions. To access our simplified data set, click the “Get the data” link in the above chart.

Ideas for classroom activities: Have students complete the data viz worksheet we provide at the top of the article, and then, based on the Data Visualization 101 activity from Visualizing the Future, have them do a quick “chart makeover.” In small groups of two to four, task students with redesigning the Chevy advertisement, producing a new version that presents the data more honestly.

Ask students to review the new charts and discuss the following:

• Does this new, more honest version still support Chevy’s claim?
• Does the new version make for a convincing advertisement?
• Do you think the makers of the chart knew that it was misleading? Why or why not?

DEFYING EXPECTATIONS

After Florida passed its “Stand Your Ground” law in 2005, gun deaths appear to drop in this graph published by Reuters. But a closer look at the y-axis reveals that the creator has inverted the scale: Instead of starting with the zero value at the bottom, the vertical axis is upside down, with zero at the top and larger numbers below—defying the conventional visual logic that “positive numbers go up.”

Casual readers may draw the wrong conclusion—that gun deaths declined after the law’s passage—when the data actually shows an increase.

“This example is a great reminder that we bring our own assumptions to our reading of any illustration of data,” explains sociology professor Lisa Wade. “The original graph may have broken convention, making the intuitive read of the image incorrect, but the data is, presumably, sound. It’s our responsibility, then, to always do our due diligence in absorbing information.”

The chart’s author, a graphics editor at Reuters, wasn’t trying to mislead readers. “I prefer to show deaths in negative terms (inverted),” wrote Christine Chan, the designer of the chart, in a now-deleted tweet. Her goal was to mimic dripping blood, a stylistic choice that ended up distorting how the data were read.

Replot the data in a more standard format, and the implications are easier to pick up.

Data notes: The data comes from official figures published by the Florida Department of Law Enforcement. To create your own chart, historical data on gun deaths can be accessed from the “Florida Statewide Murder by Firearm, 1971–2020” table available here. Data on gun deaths can also be accessed through other sources, such as the CDC’s Firearm Mortality website or the Johns Hopkins Center for Gun Violence Solutions database. To access our simplified data set, click the “Get the data” link in the chart above (all of the charts we re-created have these convenient links).

Ideas for classroom activities: Have students replot the line graph on a simple piece of graph paper, using the typical 0 value at the intersection of the x and y axes (see the above chart). To explore the importance of titles and brief explanations, ask students to write their own title and explanation for their new chart.

Alternatively, you can create small groups for a discussion of another, more sophisticated chart, comparing and contrasting the chart in “Iraq’s Bloody Toll,” by Simon Scarr, for the South China Morning Post.

Balancing the Scales

When two data sets have significantly different scales, plotting their values in the same chart can flatten meaningful differences. In the chart below, which was shared by Vermont Sen. Bernie Sanders on X, the change in average home prices, which can reach hundreds of thousands of dollars, is plotted against the change in average weekly wages, which are much smaller sums. Since home prices are much higher and swing more erratically than weekly wage increases, the graph gives a misleading impression of a growing affordability crisis for housing.

In fact, it’s entirely possible that wages have kept pace with, or increased at a higher rate than, home prices, but the design makes a fair comparison nearly impossible. To compare the two trendlines, the rates of change must be isolated first.

When isolated, weekly wages have indeed been stagnant while home prices have risen sharply, but the gap between take-home pay and housing prices is not as stark as the Sanders campaign suggests. In 1967, the inflation-adjusted median price of a home was $205,603, rising to $414,015 in 2023, a 101 percent increase. Inflation-adjusted weekly wages, on the other hand, rose from $1,020 to $1,109 in the same period, a much smaller 9 percent increase.

“No one would ever compare the price of a home to weekly wages because no one is buying a home with the money they made that week,” writes Patrick Gourley, professor of economics at the University of New Haven. It’s a misleading comparison, since it trivializes wages relative to the price of a home.

Data notes: The original tweet cites the Federal Reserve Bank of St. Louis and U.S. Census as the data sources, both of which publish official U.S. government data. For median home prices, public data from the Federal Reserve Bank of St. Louis by default isn’t adjusted for inflation, but it can be recalculated. Weekly wages (unadjusted) can be accessed from the Federal Reserve Bank of St. Louis site and adjusted for inflation (1982 dollars, multiplied by 3.04702). Depending on whether historical data is adjusted for inflation, different trends may emerge. To access our simplified data set, click the “Get the data” link in the above charts.

Ideas for classroom activities: In addition to replotting the charts using the data provided—or digging into the historical data at the U.S. Census Bureau to create more robust charts—students can discuss provocative questions like these:

• How do students interpret the replotted data? What story does the data tell—have students express their opinions using specific numbers and ratios, instead of vague terms like “bigger” or “cheaper.”
• If a politician shares a misleading chart, who bears more responsibility for checking its accuracy—the politician who posted it, or the person who sees and shares it?
• In an English or social studies classroom, analyze the language of the tweet, considering how it moves from provoking an emotional response to a moral judgment and then a policy demand.

I Need More Time

In this chart, published in 2022 by the official White House account on X, the economic growth of President Biden’s first year in office was touted as a significant achievement. Savvy chart readers will notice an odd detail: The top of the y-axis alters the interval, with 5.5 inserted between 5.0 and 6.0, an adjustment that increases the height of the final white bar.

Why make such an inconsequential tweak? One theory is that eagle-eyed readers will “tsk-tsk” the chart on social media, giving the chart more exposure without incurring a major reputational risk. It’s a reminder that charts don’t exist in isolation; they’re part of a larger media ecosystem that is constantly trying to convince audiences and tell a story through the data.

Subtle design choices can also shape how we perceive a chart. For example, a professionally designed chart can seem more authoritative, and familiar colors, fonts, or decorations—such as “presidential blue”—can make readers less likely to question the underlying data. In the economic growth chart above, the spacing between the bars and the selective use of white subtly pull the reader’s attention to the last bar.

Any chart with a series of years will also be shaped by the time frame it includes. When The New York Times plotted economic growth going back an additional two decades, a different picture emerged: The 2021 rebound, while still strong, was eclipsed by an even larger bar in the mid-1980s.

Data notes: The data can be downloaded from the U.S. Bureau of Economic Analysis, with annual figures ranging from 1930 to 2026. While the original tweet’s chart began in 2001, an earlier date can be set to present a more comprehensive picture of long-term GDP growth. To access our simplified data set, click the “Get the data” link in the above charts.

Ideas for classroom activities: Ask students to reflect on the design and data choices that may bias the reader towards the chart’s claim—directing them toward labels, increments, the unusual spacing between the bars, and the use of color, for example. Then have students replot the 20th century up to 2025, and also expand the chart into the preceding decades, including the 1980s and 1990s, for example. Discuss how the new charts influence their conclusions.

ACCURATE BUT MISLEADING

Even a chart that’s technically 100 percent accurate can be misleading. A widely shared chart on climate change plotted the average global temperatures since 1880, giving the impression that they have remained largely stable. But that conclusion is based on the scale of the y-axis, which spans an excessively large range, stretching from -10°F to 110°F. It may seem like an innocent, playing-by-the-rules design choice—after all, a truncated vertical axis is commonly linked with deception—but in this case, the distortion comes from an overextended scale that masks meaningful nuances.

Since the original chart may be difficult to read, we recreated the chart, using data from the U.S. National Weather Service. The original scale and labels are preserved, improving readability.

The pertinent information is not the average temperature, but the rate of change in the average temperature. “An increase in global average temperature of even a degree or so is a big deal, so starting the y-axis at 0°F obscures the important changes over time you’d want to see,” writes computer science professor Michael Correll. “This is why, when we design line charts, we pay special attention to the aspect ratio of our charts so that people can correctly estimate the rate of change in the data.”

Another way to think about it: consider a chart displaying human body temperatures. A shift from 98.6°F to 102°F indicates a serious medical problem but would appear flat with a large enough scale. There’s also no reason to include temperatures far outside the normal human span. In this situation, truncating the y-axis is appropriate because it focuses attention on the data range that’s meaningful, making small yet critical changes visible.

The framing also reinforces a misleading takeaway by presenting one interpretation as inevitable, creating “a clear and unambiguous logical necessity that follows from a dataset to a single conclusion,” writes Correll. The caption, “The only #climatechange chart you need to see,” suggests authority, discouraging readers from questioning the claims. But a well-designed chart invites discussion.

“I’m a great believer in the fact that good charts that are well understood can enable conversations,” says Cairo. “But charts that are badly designed—or charts that are well-designed but badly interpreted—can hinder those same conversations.”

Data notes: Average global temperatures can be downloaded from the National Oceanic and Atmospheric Administration, a federal agency that tracks global weather and climate data. When values are large—temperature data or stock market values, for example—small but meaningful changes can be trivialized. Recalculating values as changes from the baseline can make those differences easier to compare. To access our simplified data set, click the “Get the data” link in the above chart.

Ideas for classroom activities: After using our worksheet (again, see the download at the top of the article), lead a discussion about what students notice and conclude about the chart of global averages, and ask them how they might rethink or replot the data. If conversation stalls, or you want to provide a hint, introduce the idea of a “deviation chart” and ask students how it might change their approach to the data.

VOLUME DOES NOT EQUAL DENSITY

Any chart with irregular shapes—maps, bubbles, or icons—can distort perceptions if area and volume aren’t scaled proportionally. The electoral map is a perennial example: Counties are colored red or blue, conflating area with population. Because large rural areas cover more land than densely populated cities, the map visually overrepresents areas with fewer voters.

Los Angeles County, for example, has more residents than 40 U.S. states, yet it appears as a small blue patch on the map, overshadowed by large swaths of rural counties with far fewer voters.

The design of this map exploits a cognitive heuristic: We equate size with importance. Larger shapes may feel more significant, even if they ultimately represent a smaller number. A more accurate visualization would rescale the shape to states or districts by population, use a scatter plot to more accurately represent voters, or use a consistent shape to normalize the volume of each state (so that Rhode Island isn’t diminished, for example).

“The most common American election maps overstate results from rural areas because these consist of large but relatively unpopulated counties and states,” writes geography professor Eric Nost. “It’s people who vote, not land.”

Finally, we recreated the original map from the White House tweet, this time with county-level population data represented by bubbles. The larger the bubble, the more people live in that county, shifting the visual emphasis from land area to population. Students can compare the two maps and discuss how the same underlying data can be interpreted in different ways based on subtle design choices.

Data notes: The U.S. Federal Election Commission compiles official election results from state and local offices, but resources like MIT Election Data Science Lab or Ballotpedia may be easier to use if you want to download cleaned-up data. Datawrapper and other advanced visualization tools provide prebuilt Electoral College maps, which rescale states according to electoral weight. To access our simplified data set, click the “Get the data” link in the above charts.

Ideas for classroom activities: Assign students to investigate the relative population sizes of states like California, Florida, Vermont, and Wyoming—along with major city populations like Los Angeles, Miami, Burlington (Vermont), and Cheyenne (Wyoming). Using maps like the ones above, discuss the relative strengths and weaknesses of each in terms of accurately representing voting power. Which data visualization is best, and why?