How can you minimize bias in data visualization?

1 Define your purpose and audience

Before you start creating your data visualization, you need to have a clear idea of what you want to achieve and who you want to reach. Your purpose and audience will influence your choice of data sources, methods, formats, and styles. For example, if your purpose is to inform, you should use data that is relevant, reliable, and representative of the topic, and avoid using misleading or sensationalist techniques that might confuse or mislead your audience. If your purpose is to persuade, you should use data that supports your argument, but also acknowledge any limitations, uncertainties, or alternative views that might affect your credibility. You should also consider the needs, expectations, and preferences of your audience, and tailor your visualization accordingly. For example, if your audience is technical, you can use more complex or sophisticated visuals that require more interpretation, but if your audience is general, you should use simpler or more familiar visuals that convey the main message quickly and clearly.

2 Choose appropriate data and methods

Data visualization is often subject to bias due to the selection and manipulation of data and methods. To reduce bias, it is important to use data that is relevant, reliable, and representative of the topic and population of interest. Additionally, methods should be suitable, valid, and transparent for the data and purpose. Furthermore, it is important to document sources, methods, assumptions, and limitations. Providing enough information and context for the audience to understand how the data was obtained, processed, analyzed, as well as potential sources of error or bias is essential. References, citations, or links to the original data and methods should be included as well as any modifications or transformations that were made.

3 Design clear and honest visuals

Data visualization can be biased by how visuals are designed, formatted, and annotated, as well as how they are perceived and interpreted by the audience. To minimize bias, you should design visuals that are clear, consistent, and coherent; honest, accurate, and fair; and properly annotated and explained. Additionally, use annotations and explanations that are concise, relevant, honest, accurate, and fair. Following these tips can help you create visuals that are ethical, trustworthy, and effective. Nevertheless, it is important to be aware that bias in data visualization is not always intentional or avoidable. Therefore, you should always be critical and cautious when creating or consuming data visualization and seek feedback from others.

4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?