This is the fourth post in a series about how missing data can bias data-driven decisions. In this post I cover how the use of data visualizations can cause data to go missing, how visualizations themselves cause data to go missing, why accessibility matters for data visualization, how a lack of labels and scales can mislead and misinform, and what to do about it.
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