Dustin Purdy Blog

Every day, our students encounter data. They see statistics in social media posts, sports highlights, advertisements, news stories, weather forecasts, health claims, and infographics. They are constantly presented with graphs, percentages, averages, rankings, and claims about what the numbers supposedly tell us. But being surrounded by data does not automatically make someone data literate. As mathematics educators, we have an opportunity and a responsibility to help students think more critically about the data they encounter in their daily lives by asking deeper questions about the meaning behind it. Questions like: Where did these data come from and who is the source? How were they collected? What does this graph show—and what might it hide? Whose perspectives are represented? Whose are not represented? What patterns or variability do we notice? Is there potential bias? And perhaps most importantly: What conclusions can we reasonably make from the evidence? These questions are at th...

I recently joined Nicole Hamilton and Lucas Owen as a panelist in a webinar titled “An AI-Resistant Approach to Teaching Statistics,” which explored how statistics instruction must evolve in a world where generative AI can quickly perform calculations and produce answers. Rather than focusing on limiting AI, the conversation centered on designing learning experiences that emphasize human reasoning, curiosity, and authentic data exploration.One key takeaway is that statistics instruction should move beyond procedural calculations and instead focus on sense-making with real data. When students investigate authentic datasets, pose their own questions, and interpret patterns, the learning becomes far more meaningful—and much harder for AI to replace. We also discussed how student engagement plays a critical role: when students are working with data that is meaningful and relevant to them, they are far less likely to rely on AI because they are genuinely invested in exploring and understand...