Sampling Bias: When Data Collection Shapes the Story

Sampling bias is rarely obvious, and that’s exactly why it matters.

In this lightening talk , we’ll explore how limitations in data collection and sampling choices can shape the story an audience takes away, even when the analysis and visualisation are technically correct.

Using real-world examples, I’ll show how certain populations can be unintentionally over-represented, under-represented, or excluded entirely, and how visual design decisions can either mask these gaps or make them transparent.

This session focuses on awareness rather than accusation, understanding the limits of what a sample can tell us, and making those limits clearer through thoughtful data communication and visual design.

- Understand what is sampling bias and why it matters even when we don’t collect the data ourselves - Recognise how sampling bias influences the conclusions audiences draw from data - Apply techniques to communicate data limitations clearly, ethically, and without assigning blame