How to Spot Misleading Charts, a Checklist

Explore a comprehensive checklist to spot misleading charts, enabling confident and informed decision-making based on: the source, chart type, axes and message.

Charts are all around us. When communicating with data, viewing a chart instead of a table of numbers can help us very quickly understand our data, make comparisons, see patterns or trends, and use that information to make better decisions. In today’s world, the ability to swiftly make decisions and act on data is crucial. When viewing and creating charts, it’s vital that we gain the ability to critically explore and discern the integrity of the information and conclusions shown in charts. Doing this important work helps us make informed decisions. 

Many people don't realize that charts are as flexible and malleable as the written word. In the same way that words can deceive, so can charts. In a world of increasing misinformation, it is vital everyone has the skills to spot the tricks used by some. It's also possible to accidentally create misleading charts if one has gaps in their data literacy: these pitfalls outline ways to ensure our own charts fit to high standards.

To aid your thoughtful review of charts, we created a handy 4 part checklist with an easy to remember acronym, SCAM. SCAM stands for Source, Chart, Axes, and Message. Don’t be SCAM’d! Read on to learn how to spot misleading charts with confidence.

The S.C.A.M. Checklist, your guide to reviewing charts

Source. Know the Source.
Chart Design. Check the Design.
Axes. Check the Axes.
Message. Review the author’s interpretations and presentation of the chart. 

Practice and be confident

Interpreting charts can be challenging work. You can all help improve how decisions are made every time you accurately read charts or call out misleading charts that you encounter. It’s important to practice. Follow the SCAM checklist every time you see a chart in your daily life.

Share and help the community

While it’s important to think critically and ask yourself smart questions, it can be extremely helpful to analyze data with others. Discuss your interpretations and chart critiques with your peers and friends. You’ll find that you can help improve the decision-making within your organization and help stop misinformation in our communities.

Want more data resources?

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Trailhead Module: Guidelines to Recognize Misleading Charts

  • A five unit badge that will help you accurately read and present chart data and avoid misleading chart designs. A perfect follow-on after reading this blog!

Trailhead Trail: Build Your Data Literacy 

  • Seven badges that will help you explore, interpret, and communicate effectively with data. The trail covers topics such as the basics of data literacy, aggregation and granularity, well-structured data, distributions, understanding variation, and correlation and regression.

Trailhead Module: Equity and Inclusion Guidelines for Data Visualization

  • A five unit badge that will help you present data through a more diverse, equitable, and inclusive lens.

Tableau Website: Grow Your Data Skills

  • Learn about our pledge to bring data skills to 10 million people by 2027 and the many resources to help everyone grow their data skills.

Blog: The Problems with Dual Axis Charts

  • Lisa Charlotte Muth discusses the risks of using dual axis charts and offers alternative ways to present data and lists other resources to explore deeper.

Blog: Avoiding the Dual Axis Chart

  • Jon Schwabish shares why we should avoid dual axis charts and provides alternatives in presenting data.

Website: Proportional Ink

Website: Misleading Axes on Graphs

  • Reading for a module for the course Calling Bullshit at the University of Washington. The reading describes how best to use axes on graphs.

Book: Alberto Cairo (2020): How Charts Lie: Getting Smarter About Visual Information,W.W. Norton & Company

  • In-depth guide on how charts can mislead us. Helps you be a critical consumer of data visualizations.

Book: Katherine Rowell, Lindsay Betzendahyl, and Cambria Brown (2020) Visualizing Health and Healthcare Data

  • With a focus on health and healthcare, a manual that will help you learn visualization best practices and create beautiful and useful visualizations for the user.