Data visualization helps us observe, interact with, and understand data. A good visualization gives us a clear idea of what the information means by giving it visual context through maps or graphs. However, not every data visualization is designed to do so. Some visualizations can even mislead people or even propagate misinformation. This article will discuss some bad data visualization examples to identify their errors. 

What is Bad Data Visualization?

Bad data visualization is a visualization that can mislead or misinform the viewer. As Alberto Cairo mentioned in his paper “Graphic Lies, Misleading Visuals”, bad data visualization has the following properties. 

• A bad visualization hides relevant data or doesn’t show much data to mislead the viewer.
• It can show too much data or present the data inaccurately to obscure reality.
• It can use graphic forms in inappropriate ways to distort the data or obfuscate it.

Having discussed the properties of bad data visualization, let us discuss some examples of bad data visualization to identify how they mislead the viewers.

Suggested reading: Visualization Wheel by Alberto Cairo

12 Bad Data Visualization Examples

In the following sections, I will discuss 12 bad data visualization examples along with what’s wrong with them. I have collected all the visualizations from the Reddit page r/dataisugly and the copyright to all the images belongs to the particular owners.

What Does This Graph Show?

Consider the following bar graph broadcasted in a news show.

In the above image, the host probably shows the forecast of temperatures for each day of the week in Fahrenheit. However, there is no context about what the host is trying to convey. If a person looks at this visualization, they will never be able to identify if this graph represents temperature or wind speed or the number of accidents, or anything else. Hence, This visualization is hiding the relevant data.

Bad Data Visualization Example 2

Consider the following visualization showing the electricity price change over from 2004 to 2013.

In the above image, you can observe that the electricity price change in Spain till 2012 is shown yearly. After that, the horizontal scale has been changed and the price changes have been shown quarterly. Due to this, the price change for each bar graph has been reduced.

This has been done deliberately to show that the electricity price changes in the regime of prime minister Mariano Rajoy have reduced. However, the reality isn’t the same. The graph is using bar graphs in an inappropriate way to distort the data. Hence, it is an example of bad data visualization.

Bad Data Visualization Example 3 

The next bad data visualization example is the following visualization broadcasted by CBSN.

The pie chart in the above image shows the percentage of Americans who have tried marijuana in three different years. Now, a pie chart is used to show percentages of a whole and represents percentages at a set point in time. Due to this, the audience may mistake the visualization showing the following information. 

• All the people participating in the survey tried marijuana. 
• 51 percent of the population tried marijuana today.
• 43 percent of them tried it last year.
• 34 percent of them tried marijuana in 1997. 

However, the reality is entirely different. The above pie chart shows data from three different surveys. The graph is trying to show that 

• Today, 51 percent of the total population has tried marijuana. 49 percent of them haven’t.
• Last year, 43 percent of the total population tried marijuana. 57 percent of them didn’t.
• In 1997, only 34 percent of the total population tried marijuana. 67 percent of them didn’t.

Thus, the above data visualization is using graphic forms in inappropriate ways to distort the data.

The Lines are Lying

For our fourth example, let us consider the following image, which is probably showing the popularity trends of two leaders Gustavo Petro and Fico Gutiérrez.

What mistakes do you think are there in the above visualization?

• First, the visualization doesn’t specify what phenomenon it wants to explain. That’s why I had to guess that it probably shows the popularity trends of the two leaders.
• Now, let us look at the other deliberate mistake. In the graphs, you can observe that both graphs have different vertical scales. Due to this, The 3% value in the right graph is placed higher than the 25% value in the left value. If a person just looks at the graphs, they might mistake that Fico Gutiérrez has higher popularity than Gustavo Petro. However, this isn’t completely True. Thus, the above visualization presents the data inaccurately to obscure reality.  

Due to the above reasons, we can say that this image is an example of a bad data visualization.

Christians haven’t Decreased Much

Our next example of bad data visualization is the following chart broadcasted by Fox News.

In the above image, the two bar charts show the percentage of Americans that identify as Christians in 2009 vs 2019. At first glance, a change from 77% percent to 65% looks very huge. This is due to the following mistakes.

• The y-axis of the chart starts at 58. This is incorrect because a bar chart should always start at 0 because the length of the line is being used to display proportions intuitively.
• Also,  the axis is deliberately scaled so that a 12% decline is shown as over half of a decline. If you only look at the bars, you could assume that the percentage declining massively, which it isn’t True.

Don’t Tell Lies About Pakistan

Now, let us consider the following image depicting the percentage of debts owed by Pakistan to different institutions.

In the above graph, you can observe that the area of each section isn’t proportional to the percentage of debt. For example, the area depicting the percentage of debt owed to China is significantly large compared to the area depicting the percentage of debt owed to private bodies or Others. However, the debt owed to China is almost two-thirds of the debt owed to private bodies or Others.

Hence, if someone just looks at segments and doesn’t focus on the numbers, they can misinterpret that China is the biggest lender to Pakistan, which is wrong. Hence, I consider this chart an example of bad data visualization. 

Bad Data Visualization Example 7

For our next example, consider this chart from the Canadian National Broadcast Company showing their sources of funding in two financial years.

At first glance, the chart might seem okay. However, there are plenty of problems with it. 

• The Y-axis of the graph has a break in it. The lower ticks at the Y-axis are separated at $100M. After $700M, It suddenly jumps from $700M to $1,700M. Due to this, the revenue of $490M looks bigger than $1213M of government funding. Hence, it’s extremely misleading to present the scale in a way where 1.2 billion looks smaller than or almost equal to 490 million. 
• At first glance, a viewer will figure out that television revenue is the same as government funding. This is due to the fact that the blue part of the bar chart is almost equal to the length of the pink part of the bar chart due to the distortion of the Y-axis labels.
• Another major problem in the above chart is that the Revenue and advertising revenue charts should not be separate from the main bar showing total income. They aren’t two separate bars but they are just subdividing the revenue section of the bar showing the total income. The second bar is just showing how the blue part of the first bar is split and the third one is showing how the purple part of the second bar chart is split.

Thus, we can say that the above chart is an example of bad data visualization as it is intentionally misleading the viewer by distorting the elements of the chart.

Bad Data Visualization Example 8

Now, consider the following example depicting the voting percentage in Venezuelan elections.

In the above example, you can observe that Nicolás Maduro received 50.66% of the votes whereas Henrique Radonski gained 49.07 percentage of the votes. Although the difference in vote percentages is only 1.66%, the sizes of the charts make the difference look huge. Hence, it is deliberately misleading the viewer. The length of the cylindrical bars must be in proportion which is not the case here. Hence, we specify it as a bad data visualization example.

The Chart is Correct, Numbers are Lying

Our next example shows the number of goals per game in the regular season vs playoffs in NFL in different years.

In the above image, you can observe that the scales of the bars are not in proportion. For example, the 0.2 height difference from 6.0 to 5.8 is shorter than the 0.2 height difference from 6.0 to 6.2. The irregularity of the length in bars can be explained by the data given here. The data present in the source has two decimal points. Also, the length of the bars has been given in proportion to the original data which is also valid. 

However, the labels of the bars are incorrect. The labels have been created by floor rounding the original data. For example, 5.88 is rounded down to 5.8, 6.28 is rounded down to 6.2, and 6.36 is rounded down to 6.3. Now, the difference between label 6.0 to label 5.8 is actually 0.16 or 0.14, while the difference between label 6.0 to label 6.2 is 0.24 or 0.26. Which is visible in the length of the bars but not in the labels. Hence, this chart is hiding relevant data. Thus, it is an example of bad data visualization.

A Mistake That Should Have Been Avoided

The following image shows the points obtained by Charles Leclerc after three races in different years.

In the above chart, you can observe that the length of the horizontal bars isn’t in proportion to the points written on them. However, the reality is different.  If we take the bars to start at the end of the car, then the bars are of the correct lengths and are in the correct proportion. Thus, the only mistake here is that the car length is distorting the bars in a manner that changes all the aspects of the bars.

Numbers Don’t Lie. Bars Do.

The following chart shows the number of cars collecting data. Again, you can observe that the length of the bars isn’t in proportion. For example, number of cars for Tesla is 10 times larger than Xpeng. However, the length of the bar doesn’t show that. Hence, it is a bad data visualization. 

Too Much Information! Isn’t it?

Our last example of bad data visualization is pretty interesting.  Look at the following chart showing the average home prices in February in the Greater Toronto area in different years.

The above chart shows too much information that can confuse the viewer. The X-axis shows the number of units of houses sold each year in February.  The Y-axis shows the average price of the houses and the year has been labeled on the bars itself. This isn’t how the bar graphs are used.

A bar graph must show only one quantity. For example, the above chart can contain two bars for each year, one for the average price and another for units of houses sold. Then, the years can be shown on the x-axis. The label on the bar must show the value of the quantity it represents, which isn’t the case here. This is why we can consider this chart as an example of bad data visualization.

Conclusion

In this article, we discussed 12 bad data visualization examples in detail. In these visualizations, some of the errors are shown deliberately to mislead the views while others are just a mistake. This can happen with any chart you see. Hence, be careful while looking at the next chart. 

To learn more about data visualization, you can read this article on data visualization best practices. You might also like this article on how to avoid chart junk. 

I hope you enjoyed reading this article. Stay tuned for more informative articles.

Happy learning!

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