Misleading graphs
Misleading curves usually display the wrong information. In most cases, the graphical representation of data does not always display the ground’s actual values. Misleading graphs are mostly used in false advertising and create some false hope for the targeted audience. Usually, the data is distorted in a particular way and shows the opposite of the actual state. Concerned stakeholders use misleading curves to make situations look better than they are. This is achieved by omitting the baselines and adding different figures and values to create a false status. Therefore, Russia flattening the Covid 19 curve is an excellent example of a misleading curve.
Looking at the curve for the first time, it does not effectively show the intended information. This is seen from how the values are plotted against each other on both the x and y axes of the scale. Cases of infection on the ground do not rhyme with the values plotted on the curve. On several months the bar’s heights correspond with actual values until a certain point of the period. After this, the curve is seen to start flattening, which is not the actual case experienced in the country. Thus, the graph shows that the country is flattening the curve despite more infections, which is somewhat false.
In a way, the graph is misleading. This is because the actual values are not well graphed on the chart. The bars represent different values from those who are experienced in the country. The graph not only misleads Russia’s citizens but also misleads the information submitted to the world health organization (Yang et al., 2019). This gives the citizens and the world a false hope that the situation is getting better, which is wrong. It also affects how citizens tend to take care of themselves, which would result in further consequences. Hence, it eventually results in more cases than reducing hence increasing the caseload rather than handling the situation.
I would have designed the graph differently to show the values as they are on the ground. Mixing of both line and bar graph types would bring out the data. Other variables, such as age, would have been used to plot the current state of the country’s infections. This will ensure the citizens get the correct information about the current state of the country. I would also use various labels such as citizens and non-citizens to make sure the correct data is captured within the graph. Therefore, by plotting the correct graph, the citizens and the entire world will know the state of affairs within Russia.
https://towardsdatascience.com/stopping-covid-19-with-misleading-graphs-6812a61a57c9
References.
Yang, B. W., Restrepo, C. V., & Stanley, M. (2019). The Misleading Effect of Truncating Bar Graphs.