You can still have powerful visualizations if you have a powerful result to relay. You can fool some of the people some of the time, but it is tough to repeatedly fool the same people over a working lifetime. "Honesty is the best policy" because generally people will get repeated exposure to you and your work. but if people realize you were trying to trick them, you build distrust in you specifically. Using graphical tricks to make your results look more shocking will get attention.
You want people to pay attention to your results, not dismiss them as boring. If you are a numbers person, you generally want to be effective. With the spread of ease in creating graphs, more people have been catching onto the tricks.
You may make a great hit with a sensational graph in the short run, but over-hype erodes itself in the long run. With modern software, it is even easier to construct misleading graphics than the hand-drafted examples of mid-20th century.
The classic book How to Lie With Statistics, first published in 1954, dedicated a whole chapter to misleading through graphs. Above we saw how one could visually exaggerate the impact of COVID in New York city by cropping an axis. Of course, that visual processing power can be misled with graphics. Humans have a great deal of innate processing power built into their brains just to interpret visual information, and it influences us in a way written text does not. Visualizations can be very powerful in influencing, especially for important decisions. This sort of modeling influenced key decision-makers in setting policy in order to reduce deaths due to the outbreak. One of the more infamous visualizations right now regards projections of resource need and number of deaths, from the Institute for Health Metrics and Evaluation: COVID-19 Projections. They note a variety of dashboards, from the well-known Johns Hopkins dashboard, to an IBM/Weather Channel collaboration, to graphics based on EMS data. The Spread and Influence of Data VisualizationĪccording to ZDnet, the COVID-19 data sets may be the most visualized infectious disease outbreak ever. You can still see the jump in deaths, and the proportion of the jump is now in line with what actually happened. This version is uncropped, with a vertical axis that starts at zero deaths.