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. The labels have been created by floor rounding the original data. However, the labels of the bars are incorrect. Also, the length of the bars has been given in proportion to the original data which is also valid. The data present in the source has two decimal points. The irregularity of the length in bars can be explained by the data given here. 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. In the above image, you can observe that the scales of the bars are not in proportion. Now, consider the following example depicting the voting percentage in Venezuelan elections. 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. 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. They aren’t two separate bars but they are just subdividing the revenue section of the bar showing the total income. 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.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. At first glance, a viewer will figure out that television revenue is the same as 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. Due to this, the revenue of $490M looks bigger than $1213M of government funding. After $700M, It suddenly jumps from $700M to $1,700M. The lower ticks at the Y-axis are separated at $100M. The Y-axis of the graph has a break in it.However, there are plenty of problems with it. The Lines are Lyingįor our fourth example, let us consider the following image, which is probably showing the popularity trends of two leaders Gustavo Petro and Fico Gutiérrez.Īt first glance, the chart might seem okay. Thus, the above data visualization is using graphic forms in inappropriate ways to distort the data. In 1997, only 34 percent of the total population tried marijuana.Last year, 43 percent of the total population tried marijuana.Today, 51 percent of the total population has tried marijuana.The above pie chart shows data from three different surveys. However, the reality is entirely different. 34 percent of them tried marijuana in 1997.51 percent of the population tried marijuana today.All the people participating in the survey tried marijuana.Due to this, the audience may mistake the visualization showing the following information. Now, a pie chart is used to show percentages of a whole and represents percentages at a set point in time. The pie chart in the above image shows the percentage of Americans who have tried marijuana in three different years. What Does This Graph Show?Ĭonsider the following bar graph broadcasted in a news show. I have collected all the visualizations from the Reddit page r/dataisugly and the copyright to all the images belongs to the particular owners. In the following sections, I will discuss 12 bad data visualization examples along with what’s wrong with them. Suggested reading: Visualization Wheel by Alberto Cairo 12 Bad Data Visualization Examples Having discussed the properties of bad data visualization, let us discuss some examples of bad data visualization to identify how they mislead the viewers. It can use graphic forms in inappropriate ways to distort the data or obfuscate it. It can show too much data or present the data inaccurately to obscure reality.A bad visualization hides relevant data or doesn’t show much data to mislead the viewer.As Alberto Cairo mentioned in his paper “Graphic Lies, Misleading Visuals”, bad data visualization has the following properties. A Mistake That Should Have Been Avoidedīad data visualization is a visualization that can mislead or misinform the viewer.The Chart is Correct, Numbers are Lying.
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