![]() ![]() ![]() Leaving out variables can affect how you interpret the data and what conclusions you draw from it. That's because by omitting some data we are missing the context. Why lie when you can just omit? By omitting certain data points, trends that don’t actually exist can easily be created whereas some existing highlights can go unnoticed. Sometimes these distortions are done on purpose to mislead readers, but other times they’re just the consequence of not knowing how an unintentional use of a non-zero-baseline can skew data. Learn how to spot the common tricks used to manipulate data and how to avoid the pitfalls for your own visuals.įocus on creating your data visualizations using data with a zero-baseline y-axis and watch out for truncated axes. Here, we will present a mix of the most common visual data misrepresentations together with practical tips on how not to fail when presenting data. The best way to safeguard from misinformation is to arm yourself with tech-appropriate analytical online data visualization tools and evaluative skills that will expose the most oversimplified or malicious data visualizations. But, by knowing what to look for, you can avoid connecting with metrics that will lead your organization down the wrong path. But while that may be the case, people are duped by data visualizations every day.įrom political issues to sports statistics and the recent report you received on the ROI of your company blog, the internet as well as informational reports are flooded with examples of misleading data visualization.īad data visualizations come in many forms, with some more obvious than others. Nobody likes feeling manipulated in any way, shape, or form. 1) Misleading Data Visualization Examples
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