"Just the facts, maam": How to identify and avoid bias in visualization of data
You know the phrase, "There's lies, there's damn lies, and there's statistics!" The problem with statistics is that it's how they are represented. People can use a set of techniques for good - representing the data effectively and avoid misinterpretation. Or they can use a different set of techniques for evil - intentionally misrepresenting the data to cause an emotional reaction and manipulate an audience.
Dr. Eleanor Small from Johnson & Johnson shares her personal experience in identifying and mitigating and/or avoiding bias in visual data, and she discusses what to look for in both good practices and manipulative practices. She will help you understand how to create GOOD graphics that intentionally avoid data bias. This includes the proper application of techniques to best illustrate different types of data so it is clear and intentionally not misinterpreted. She also discusses the bad. Beyond explaining the techniques people use to sway opinions with graphics, Dr. Small shows how it's actually being used on you every day. She shows the ACTUAL charts and graphs created by politicians and the news media to manipulate their audience, and it literally go case-by-case through each type technique.
The hope of this session is that you will know what to look for when somebody pulls up a graph to make their point on social media or the news. As a general rule, when a graphic causes an emotional reaction, a healthy dose of skepticism is nearly always warranted.
- Recognize presenter bias in graphs, graphics, and tables, and discern the hidden message
- Learn to choose the appropriate manner to display your data between tables and various graph types.
- Learn how to utilize insets, out-takes, and other techniques to “tell the story” of highly differentiating results
- Prepare clear and robust data presentations which can withstand common problems of color misalignment, out of focus, accidental black and white.
- Effective Metrics and Reporting Styles – Identifying Bias in Graphs and Metrics
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