Key Takeaways:
- Graphs are powerful tools for conveying data, but they can be manipulated to fit an agenda and mislead the audience.
- Bad graphs often misuse visual proximity, manipulate data, and omit important details, leading to misinterpretation.
- Examples of bad graphs include those with skewed y-axis scales, misleading percentages, and manipulated axes.
- It is essential to critically evaluate graphs, consult the actual data, and follow best practices to ensure accurate representation.
Introduction
Graphs are widely used to present data in a visual and accessible format. They have the potential to simplify complex information and convey it to a broad audience effectively. However, graphs can also be manipulated to mislead and distort the data they represent. In this article, we delve into the world of bad graphs, exploring the creative ways they can be used to mislead and distort information. By understanding the techniques used in bad graphs, we can become more critical consumers of visual data and better equipped to navigate the realm of data representation.
An Act of Omission: Skewed Y-Axis Scales
One common technique used in bad graphs is manipulating the y-axis scale to misrepresent data. By adjusting the intervals or scaling of the y-axis, graph designers can create a distorted visual representation of the data. This manipulation can make differences in data appear larger or smaller than they actually are.
For example, consider a graph posted on Twitter with an uneven y-axis scale. The graph may appear flattened due to the irregular intervals on the y-axis. To illustrate this point, let’s compare the graph with corrected intervals on the y-axis to a logarithmic scale graph similar to one used by a news outlet:
Corrected graph intervals (left) vs. Logarithmic scale (right)
Using logarithmic scales is appropriate when comparing orders of magnitude, such as comparing the weights of mice and elephants. However, in the case of the bad graph, the use of a logarithmic scale was unwarranted and misleading. The graph failed to disclose its quasi-logarithmic scale, leading to misinterpretation of the data. Additionally, starting the scale at a value higher than zero (e.g., y = 30) can further distort the representation of the data.
To avoid falling victim to graphs with skewed y-axis scales, it is crucial to critically examine the intervals and ensure that the scale accurately represents the data being presented.
The “Data” is In…accurate: Misleading Percentages
Some graphs are intentionally created to mislead, while others result from misunderstandings or misinterpretations of the data. One such example is a graph shared by a public figure on Twitter, suggesting a decline in Covid-19 cases in a specific location:
At first glance, the graph may appear to indicate a decline in cases. However, the issue lies in the way the data was collected and presented. In this case, the graph represents percentages rather than absolute counts. Each positive test result is counted once, while each negative result is also counted once. However, this counting method becomes problematic when presented as percentages.
Consider a scenario where a person tested negative multiple times. Each negative result would be counted separately, leading to an inflated count of negative cases. The graph fails to account for this discrepancy, resulting in a misleading representation of the data.
It is essential to be cautious when interpreting graphs that present percentages without accounting for potential biases in data collection or counting methods. Consulting multiple reliable
sources and verifying the underlying data can help ensure a more accurate understanding of the situation.
Georgia’s Trends are Just Peachy: Time Traveling Axes
Another technique employed in bad graphs is manipulating the x-axis to create a misleading visual effect. In the case of a graph from the state of Georgia, the graph used a time-traveling axis, leading to a confusing representation of the data.
According to the Associated Press, the graph from Georgia arranged the dates on the x-axis in a non-chronological order, creating a staircase-like effect. Dates appeared out of sequence, with some dates shown before others that should logically come before them. This manipulation of the x-axis created a distorted narrative and compromised the integrity of the data.
To avoid being misled by time-traveling axes, it is crucial to critically examine the order and sequence of dates in a graph. Consulting reliable sources and cross-referencing data can help ensure the accuracy of the information presented.
Best Practices for Accurate Data Visualization
Given the prevalence of bad graphs, it is crucial to adopt best practices for creating and interpreting data visualizations. Here are some key guidelines to consider:
1. Ensure the Right Scale
Make sure the vertical scale accurately represents the data being presented. Avoid manipulating the scale to exaggerate or minimize differences.
2. Don’t Skip Numbers
Maintain a consistent scale without skipping intervals. Skipping numbers can distort the representation of the data and lead to misinterpretation.
3. Start at Zero
When using a vertical scale, begin at zero to provide an accurate baseline for comparison. Starting at a value higher than zero can create a misleading visual effect.
4. Include Informative Labels
Label your axes clearly and provide informative titles, captions, and legends. These labels should accurately represent the variables being measured and provide context for the data.
5. Use All Available Data
Ensure that your graph includes all relevant data points. Omitting or selectively including data can skew the representation and mislead the audience.
Conclusion
Graphs are powerful tools for data visualization, but they can also be manipulated to mislead and distort information. Bad graphs often misuse visual proximity, manipulate data, and omit important details, leading to misinterpretation. By understanding the techniques used in bad graphs and adopting best practices for data visualization, we can become more critical consumers of visual data. It is essential to consult reliable sources, verify underlying data, and critically evaluate graphs to ensure accurate representation and make informed decisions based on the actual data.
