Not all misleading charts are intentional. Often, they’re the result of small visual design decisions that unintentionally distort how data is interpreted.

In this session, we’ll explore real-world examples of misleading charts from media, public reports, and widely shared graphics. From pie chart overload to truncated axes, confusing colour choices, and questionable scales, we’ll examine how seemingly innocent visuals can change the story data appears to tell.

Using these examples as a starting point, we’ll translate the lessons into practical guidance for developers working with Power BI. We’ll look at how chart selection, scale configuration, colour usage, and layout decisions can influence interpretation, and how to design visuals that communicate clearly and responsibly.

We’ll also touch on concepts such as framing effects and sampling bias, exploring how cognitive and visual factors combine to shape the narratives audiences take away from data.

By the end of this session, you’ll have sharper instincts for spotting misleading visuals and practical techniques to ensure your Power BI reports support clear, trustworthy insights.