MODULE 5 – Persuasion Versus Deception
In our last blog post we explored some best practices and common mistakes in crafting data visualizations and infographics. In this post we are going to take some of those principles further by applying the art of persuasion to our graphics and examine when data visualizations cross the line into deception. Practice is “how”, purpose is ‘why”.
Science versus Advocacy
Science is based on the search for truth. The scientific method is unproven theory backed up by sound empirical evidence. That is why it takes so long for “theories” to mature into “laws”. Einstein determined the theory of relativity in 1905 and we are still waiting for it to become law. Data scientists present all the data, as it must be reviewed, challenged and verified. Scientific analysis is dispassionate, methodical and best served cold. It needs to be clear, fair and impartial to agendas and goals. It cannot take positions….it simply seeks to show and allow the audience to develop its own interpretation.
Advocacy on the other hand, runs hot. Advocacy believes an idea, takes a position and seeks to persuade an audience of the validity of a specific viewpoint. Some may argue that any declarative chart is an act of persuasion, but there are ways in which data visualizations can move from reporting facts and information to convincing an audience or supporting and argument.
Your first step is to determine the goal of your visualization, are you reporting or convincing? If you are attempting to persuade your audience, then you know you should be in advocacy mode.
The child of two disciplines, Graphic Design and Science, DataViz is a form of visual communication, in which the following design methods can be used to persuade:
- Hone your main idea. Use your headline, title, text to support the perspective.
- Make it stand out. Use unique colors, labels, marks to draw the audience focus to the data that supports the argument.
- Isolate or highlight the main idea by reducing other data points attributes, deemphasize them with color choices or composition. For example, making other values gray or desaturating them in a line chart or changing shape in a flow chart.
- Removing, adding or shifting reference points can alter the audience perspective, clarify the position and support the argument. For example, seeing a trend in sales may be easier when you add or subtract years to your X-axis. Pointing out a behavioral trend may be easier to show when comparing 1 to 10 rather than 1 to 100.
Refining your charts to persuade in this manner can really make them effective in any situation where you need to advocate or defend ideas. There are many valid applications for these practices which include but are not limited to sales presentations, advertising, legal matters, and policy decision discussions.
We live in an age where images can be manipulated in order to deceive. Data visualizations and infographics are no exception. We have photoshopped magazine covers, special effects, deep fakes…technology has made it easier to lie to our eyes. Just as in other familiar kinds of deception, we will find falsification, exaggeration, omission and equivocation. In addition, Mr. Edward Tufte identifies five more specific methods of distorting presentations. These are effects without causes, cherry picking, overreaching, distracting chart junk and the rage to conclude. In “Beautiful Evidence” Tufte urges us to sharpen our critical analysis of visual presentations. He warns of bad proxies in which unreasonable approximations are made for data points and arguments. ”Economisting”, a term used to describe the act of taking limited evidence and moving to conclusions that are theory driven rather than evidence based and self-validation are also traps. In his book “Good Charts”, Scott Berinato presents some of the more common ways charts can be misleading or present a false narrative:
- The Truncated Y-axis: Removing valid value ranges from the visual field in order to present a more dramatic visual effect for a trend, comparison or anomaly.
- The Double Y-axis: Charts that include two vertical scales for different data sets producing a false or artificial relationship between the two. Even when there are valid measurements and seemingly related or parallel data trends, correlation does not mean causation.
- Map Disparities: When presented in certain ways, geographic boundaries and points may not include additional data values that may be counter to the argument. Geography may skew the viewer’s perspective on proportions to the whole or concentrations. For example, states may have a larger land mass but a smaller population than cities allowing for economic, crime and other social reporting out of context.
It is important to understand that sometimes these manipulations are not always performed knowingly and inaccurate visualizations may stem from an earnest enthusiasm for the idea or theory. People want to believe they are right or favor “good news” which may insert bias into the collection method or execution of the piece. Tufte elaborates on the perils of evidence construction:
“Between the initial data collection and the published report, the shadow of the evidence reduction, construction and representation process: data are selected, sorted, edited, summarized, massaged, and arranged into published graphs, diagrams, images, charts, tables. Numbers, words. In This sequence of representation, a report represents some data which represents the physical world.”
Many decisions affect the final presentation, so it is key to seek assurance on the integrity of the process. In fact, this blog post summary of the readings is just a distillation of a very thorough clinic provided by Mr. Tufte on good analysis. Please go to the source, read the original, the links are provided in the references below.
Ethical Considerations and Guidelines
The line is not always clear on when it comes to DataViz persuasion versus manipulation. Luckily we have some questions we could ask ourselves when we find ourselves in a gray area and have some doubts. They include:
- Does my chart show the idea or change the idea? If it changes the idea, is it contradicting the original idea?
- Am I hiding something that would rightfully challenge the argument being made?
- Would I feel lied to if someone else presented something in different circumstances?
- Could you defend this presentation and yourself in the event of a challenge to its credibility?
- Has the data/evidence been gathered in a bias manner?
- Who has provided or paid for the data?
- Does the conclusion come from the data/evidence or data/evidence construction?
- Do you have access to all the data or original sources?
In addition, depending on the context, if you feel that a specific chart may be misinterpreted or perceived as misleading, it may be helpful to include additional charts or graphics that will support the discussion and have your source data available for reasons of transparency.
Demonstration – Global Temperatures and shifting reference points
For my final data project I am planning on creating a “Data Story” which is a dashboard that presents a persuasive story or presents a narrative through several data visualizations. My topic consists of several environmental data points including Global Temperatures and CO2 Emissions. “Our World in Data” is a great project that presents this data online. I will use this as a quick example of how visualizations can be manipulated to present misleading arguments.
In Example A, “Global Temperatures are Falling” the data is correct. These have been the figures since 2015. The chart can be used to support an argument that global temperatures are cyclical and that they have in fact been dropping over the past few years. Couple this chart with some other data about green efforts and you may have a very optimistic view of the situation. I have also narrowed the width of the chart and started the y-axis at 0.4 Co in order to “emphasize” the falling temperature readings. These are a few methods a designer might use to make a presentation more dramatic.
However, in Example B. “Global Temperature is Rising”, we examine all the data and widen the reference by adding more years. We then get a different picture. This is an example of “shifting the reference point”. Now we are including all the data from 1950 – 2018 and the chart clearly shows that despite the seasonal peaks and lows in temperature change, the median global temperature has been steadily on the rise.
This is just a quick and dirty example generated from the data and pushed out quickly in Powerpoint, but it demonstrates how easily charts and graphs can be misrepresentative of the facts. Visuals can be powerful and convincing artifacts. They can be used effectively for good and noble purposes such as when Dr. John Snow saved lives by charting the source of a cholera epidemic in London in 1854, or when diagrams of transatlantic slave ships were published in London in 1789 and the engravings became the icon of the antislavery movement in England and the United States. That same power can be abused, so with it comes great responsibility.
References and Resources for Further Exploration
Tufte, E. R. (2006). Beautiful Evidence. Graphics Pr. Retrieved from https://www.amazon.com/Beautiful-Evidence-Edward-R-Tufte/dp/1930824165 Pages 141-156
The Work of Edward Tufte and Graphics Press. (2019, June 23). Retrieved from https://www.edwardtufte.com/tufte/?gclid=CjwKCAjwxrzoBRBBEiwAbtX1n8anMR_s-KFh7XOZdEyUmByMECm1jVyBkzAJj_HD__UNiF5_7a0y2RoCdnUQAvD_BwE
Tufte, E. R. (1997). Visual Explanations: Images and Quantities, Evidence and Narrative. Graphics Press. Retrieved from https://www.amazon.com/Visual-Explanations-Quantities-Evidence-Narrative/dp/1930824157 Pages 27-37
Ritchie, H., & Roser, M. (2017). CO₂ and other Greenhouse Gas Emissions. Our World in Data. Retrieved from https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions
Visually Blog 12 Great Visualizations That Made History | Visually Blog. (2019, June 23). Retrieved from https://visual.ly/blog/12-great-visualizations-that-made-history
Berinato, S. (2016). Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations. Harvard Business Review Press. Retrieved from https://www.amazon.com/Good-Charts-Smarter-Persuasive-Visualizations/dp/1633690709
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