Evaluating Data Visualizations
As an information consumer, you need to be critical of data visualizations like any other information source.
Although the information is presented in an eye-catching way, it is possible for the data to be misinterpreted, over-simplified or over-complicated.
Below are some tips to help guide you through the evaluation process.
Tips to evaluate Data Visualizations
1. Truncated axis
The baseline for any graph should begin a "0" for both the X and Y axis. When a graph begins at a number other than zero, the data can be skewed and misinform the reader.
2. Manipulating the scale
The scale for the both the X and Y axis should increase proportionately in order for the reader to accurately interpret any changes in the data. Compressing or expanding the scale can make the changes in the data seem more or less significant than they actually are.
3. Cherry picking data
Sometimes designers only include certain data points to reinforce the intent of the graph. Any missing data should be indicated in the rather than completely excluded.
4. The wrong graph for the data
It is important to choose the right graph for the data. If you use the wrong graph, the data can be skewed which can confuse the reader.
5. Failed calculations
Failed calculations are often represented in pie charts. Slices should always add up to 100 and show parts of a whole.
6. Correlation implying causation
Just because two sets of numbers follow a similar path doesn’t mean there’s a correlation.
7. Violating standards of conventions
Over time, data visualizations have established standards which readers expect when they read any visualization. For example, on a map visualization, the darker the shade indicates a higher level of density. Graphs that reverse this convention can confuse the reader and cause them to misinterpret the information.
8. Too hard to understand
Visualizations should make data easier to understand and not confuse the reader. If the visualization includes too much data, colors, etc. the reader will have to work too hard to understand the message of the graph.
9. Bias
Purposeful Bias is a deliberate attempt to influence data, most likely to take the form of data omissions or adjustments.
Selective Bias is slightly more discreet where the sample population is not representative of the population at large or the population of interest. One example is: Surveying college students about legal drinking age.
Content for this list was compiled from: 5 Ways Writers Use Misleading Graphs To Manipulate You and https://guides.zsr.wfu.edu/interpretdataviz
Examples of Poorly Designed Visualizations
Inverted axis
Pie Chart Adds up to more that 100
Bar heights should represent value
Line Chart should follow data over time
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Designing Visualizations Books
The Wall Street Journal Guide to Information Graphics by
The definitive guide to the graphic presentation of information. In today's data-driven world, professionals need to know how to express themselves in the language of graphics effectively and eloquently. Yet information graphics is rarely taught in schools or is the focus of on-the-job training. Now, for the first time, Dona M. Wong, a student of the information graphics pioneer Edward Tufte, makes this material available for all of us. In this book, you will learn: to choose the best chart that fits your data; the most effective way to communicate with decision makers when you have five minutes of their time; how to chart currency fluctuations that affect global business; how to use color effectively; how to make a graphic "colorful" even if only black and white are available. The book is organized in a series of mini-workshops backed up with illustrated examples, so not only will you learn what works and what doesn't but also you can see the dos and don'ts for yourself. This is an invaluable reference work for students and professional in all fields.
Call Number: HF5718.22 .W65 2013ISBN: 9780393347289Publication Date: 2013
How Charts Lie by
We've all heard that a picture is worth a thousand words, but what if we don't understand what we're looking at? Social media has made charts, infographics, and diagrams ubiquitous--and easier to share than ever. We associate charts with science and reason; the flashy visuals are both appealing and persuasive. Pie charts, maps, bar and line graphs, and scatter plots (to name a few) can better inform us, revealing patterns and trends hidden behind the numbers we encounter in our lives. In short, good charts make us smarter--if we know how to read them.However, they can also lead us astray. Charts lie in a variety of ways--displaying incomplete or inaccurate data, suggesting misleading patterns, and concealing uncertainty--or are frequently misunderstood, such as the confusing cone of uncertainty maps shown on TV every hurricane season. To make matters worse, many of us are ill-equipped to interpret the visuals that politicians, journalists, advertisers, and even our employers present each day, enabling bad actors to easily manipulate them to promote their own agendas.In How Charts Lie, data visualization expert Alberto Cairo teaches us to not only spot the lies in deceptive visuals, but also to take advantage of good ones to understand complex stories. Public conversations are increasingly propelled by numbers, and to make sense of them we must be able to decode and use visual information. By examining contemporary examples ranging from election-result infographics to global GDP maps and box-office record charts, How Charts Lie demystifies an essential new literacy, one that will make us better equipped to navigate our data-driven world.
Call Number: P93.5 .C26 2019ISBN: 9781324001560Publication Date: 2019
