# The Art of Misleading Graphs: How Data Visualization Can Distort the Truth

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## Key Takeaways:

• Graphs are powerful tools for conveying data, but they can be misused to mislead rather than inform.
• Misleading graphs can manipulate visual proximity, distort data, and omit important details.
• Examples of misleading graphs related to COVID-19 include the misuse of logarithmic scales, inaccurate data representation, and manipulation of timeframes.
• When interpreting graphs, it’s crucial to consult reliable data sources, examine the scale and labels, and consider the complete dataset.

## Introduction

Graphs are an essential tool for visually representing data and conveying complex information in a simplified manner. They are widely used in various fields, from scientific research to business presentations, to help audiences grasp trends, patterns, and relationships. However, as the saying goes, “There are three kinds of lies: lies, damned lies, and statistics.” Graphs, like any form of visual representation, can be misused and manipulated to distort the truth. In this article, we will explore the world of misleading graphs, focusing on examples related to the COVID-19 pandemic. By understanding how graphs can be misleading, we can become more critical consumers of visual data and avoid being misled by deceptive representations.

## An Act of Omission

One common way that graphs can be misleading is through acts of omission. This occurs when important information is deliberately left out or misrepresented, leading to a distorted interpretation of the data. Let’s examine an example related to COVID-19.

The graph in question was posted on Twitter by @Carnage4Life, with the caption “You can teach an entire semester of how to lie with statistics with the y-axis of this chart.” Upon closer inspection, we can identify the problem. The y-axis of the graph has uneven intervals, giving it a flattened appearance. To provide clarity, we can recreate the graph with correct intervals on the y-axis. Additionally, the original graph used a logarithmic scale similar to the one used by a Fox News graphic. Logarithmic scales are appropriate when graphing orders of magnitude, but they must be labeled accurately to avoid misleading interpretations. In this case, the scale was not clearly labeled, and it started at y = 30, potentially minimizing the significance of data points.

## The “Data” is In…

In some instances, misleading graphs are not a result of intentional deception but rather a misinterpretation or misrepresentation of the data. Let’s examine a graph shared by Jacksonville FL mayor Lenny Curry on Twitter (@lennycurry) that appeared to show declining COVID-19 cases in Florida.

At first glance, the graph seems unremarkable. However, the issue lies in the data behind it. During the early stages of testing in Florida, strict requirements were in place, leading to limited testing and a skewed representation of COVID-19 cases. As testing restrictions loosened and became more widespread, the number of negative test results also increased significantly. However, the graph only displays percentages, not counts, making it problematic. Negative results were counted multiple times, creating a misleading perception of declining cases. Whistleblower Rebekah Jones, a former employee of the Florida Department of Health, highlighted these discrepancies and provided alternative figures on her website. The conflicting interpretations of the data underscore the importance of accessing accurate and comprehensive datasets and the challenges of navigating conflicting information.

## Georgia’s Trends are Just Peachy

Another tactic used in misleading graphs is the manipulation of axes and timelines. The State of Georgia found itself embroiled in controversy when a graph surfaced, showcasing a “staircase” effect caused by inconsistent ordering of dates on the x-axis.

According to the Associated Press, Georgia’s graph used time travel, with dates hopping back and forth along the x-axis, creating a distorted representation of the data. The colored bars on the graph were also arranged differently for each date, further complicating the interpretation. State Representative Jasmine Clark criticized this manipulation, emphasizing the importance of data integrity and accuracy. The AP recreated the graph with the correct ordering of dates, demonstrating the misleading nature of the original representation.

## Conclusion

Misleading graphs can have far-reaching consequences, especially in the context of a global pandemic like COVID-19. They can create confusion, sow doubt, and even contribute to the spread of misinformation. As consumers of visual data, it is essential to approach graphs with a critical eye and be aware of the techniques used to distort information. To avoid being misled, consider the following best practices:

• Verify the data sources and consult reliable information.
• Examine the scale and labels of the graph for accuracy.
• Consider the complete dataset rather than relying on selective or partial representations.
• Be aware of the potential biases and agendas behind the graph’s creation.

By fostering a critical mindset and promoting data literacy, we can navigate the world of graphs more effectively and make informed decisions based on accurate information. Remember, the true power of graphs lies in their ability to inform and enlighten, but it is up to us to discern the truth behind the visual representations.