Charts and graphs are important in conveying compound statistics to a general population. However, some people manipulate graphs to mislead the general audience towards making wrong inferences. Truncated graphs have manipulated axis to distort the visualization. Misleading graphs may be the byproduct of incorrect computations or intentional manipulation to sell a particular agenda. It is important to address issues with misleading graphs to ensure users can draw correct inferences.
Screenshot of the Graph
Figure 1 : Stop Covid-19 with Misleading Graphs.
Source: https://towardsdatascience.com/stopping-covid-19-with-misleading-graphs-6812a61a57c9
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The reason why the Graph is Misleading
People can manipulate an axis in a graph to distort and manipulate the presentation of the graph. For instance, an Argentinian TV Channel, C5N, manipulated the Y-axis in the graph illustrated above to hide the poor number of COVID-19 test. The graph illustrates the number of coronavirus tests per million people. From the graph the Noruega is Norway, Alemania is Germany, and EEUU is the United States. The U.S. tests 7000 people per million, while Argentina tests 330. The numbers for the U.S are twenty times greater than those of Argentina. However, America's bar is merely 1.2 times higher (Kotsehub, 2020). The graph is poorly constructed without regard for the appropriate scaling of the Y-axis. The misrepresentation of data distorts the graph and its use can lead to incorrect derivation. For instance, an individual looking at the graph may assume that Argentina's capacity to test people is good when in reality it is poor especially when compared to other nations such as Italy and the United States of America.
Analysis
Fixing the graph’s Y-axis will offer a better illustration of the data to ensure the illustration is not misleading. This can be accomplished by developing an effective scale on the Y-axis. For instance, applying a scale of 5,000 can help offer a better illustration of the testing capacity of the different nations (see Figure 2 ). Addressing the Y-axis will help to remove the manipulations from the graph. The visualization displayed by Figure 2 demonstrates that Argentina’s COVID-19 testing capacity is poor and cannot be compared to those of other nations such as Norway, Italy, Germany, and the United States (Kotsehub, 2020). This visualization is different from that demonstrated by Figure 1 and people can draw a more accurate and conclusive conclusion on Argentina's testing capacity from Figure 2. Accurate data representation and visualization is important to ensure that graphs are accurate which allows users to draw accurate inferences.
Figure 2: Stop Covid-19 with Misleading Graphs.
Source: https://towardsdatascience.com/stopping-covid-19-with-misleading-graphs-6812a61a57c9
Argentina's C5N has manipulated the graph to distort its massage and make Argentina look decent at testing citizens when this is not the case. The TV station illogically manipulated the bars' size without regarding the rules of scaling. C5N truncated the graph to manipulate data to sell a particular agenda. A truncated graph is one that has a manipulated graph of the axis to distort the presentation of data ("How Graph Misrepresents Data," 2020). The television channel truncated the graph to present a narrative that Argentina's capacity to test COVID-19 was comparable to that of other countries such as the United States. However, this illustration was misleading because Argentina’s testing capacity is 20 times worse than that of the United States.
Conclusion
The Argentinian TV Channel, C5N, manipulated the Y-Axis of a COVID-19 testing capacity to sell the narrative that the nation's testing capacity was comparable with that of other nations such as the United States. However, Argentina's testing capacity was 20 times worse than that of the U.S. This graph can be corrected by adjusting the scale on the Y-axis. It is important to ensure correct visualization to make sure the general audience can draw accurate conclusions from the graph.
References
“ How Graph Misrepresents Data.” (2020). Press Books , https://ecampusontario.pressbooks.pub/bio16610w18/chapter/how-graph-misrepresents-data/
Kotsehub, N. (2020). Stopping COVID-19 with Misleading Graphs. Towards Data Science , https://towardsdatascience.com/stopping-covid-19-with-misleading-graphs-6812a61a57c9
