This two-day executive education course will give journalists working knowledge of key principles that allow them to ask questions that separate credible from non-credible evidence, distinguish causation from correlation, and avoid common pitfalls in interpreting quantitative evidence. Through a combination of in-class instruction, pertinent examples, and experiential group exercises, participants leave the course with a transformed understanding of how to extract credible, substantive, and reliable information from quantitative analyses and data. Day One Morning, 9am – 12:30pm: Creating Shared Language Key Takeaway It can be difficult to distinguish between correlation and causation; knowing how to do this is essential because when we get it wrong, you reach false conclusions. The stakes are high! Agenda Introductions Is this causal? Break Correlation & Causation: What are they and what are they good for? Correlation and Causation: Why knowing the difference matters Lunch, 12:30 – 1:30pm Afternoon, 1:30 – 5pm: Correlations, the Cornerstone of Quantitative Analysis Key Takeaway Correlation is the cornerstone of all quantitative analysis. Establishing a correlation is not as easy as we may think, you have to look at the right kinds of evidence. Agenda Post-Mortems and Lessons Learned Is this a correlation? Correlation Requires Variation: Challenges to establishing correlation Break Post-Mortems revisited Tying it all together Welcome Reception Day Two Morning, 9am – 12:30pm: Questioning Causality Key Takeaway Sometimes correlation is causation, but you can’t trust an interpretation of causal relationships without accounting for confounders. When using evidence of a correlation to gauge the effect of a planned action, it is critical to assess whether a causal interpretation of that correlation is credible/plausible. To do so, ask: Are there unaccounted confounders? Is there reverse causality? The first line of defense against mistaken correlation for causation is to control for confounders. The problem is that many potential confounders are unobservable. Agenda When correlation isn’t causation: the problem of confounding variables. The experimental ideal. Making valid comparisons, drawing valid conclusions. Confounding variables and selection effects. Break Now what? Questions you should ask when someone purports to give you evidence of a causal relationship. Thinking through confounding variables. Controlling for confounders. Lunch, 12:30 – 1:30pm Afternoon, 1:30 – 5pm: Avoiding Pitfalls Key Takeaway The world can create misleading or false patterns. Some of the standard practices of science exacerbate such problems. It is important to remain skeptical, especially about surprising findings. Before reaching an evidence-based conclusion, be sure to turn statistics into substance and ask whether you’ve measured the right outcome. Agenda Over-Comparing, Under-Reporting Mean Reversion Publication bias: Why most scientific findings are false Break Turning Statistics into Substance Measuring the mission Pulling it all together Foundations of data journalism Apply now to study the foundations of evidence in data journalism. Apply Now Recent News Harris Professors Offer Advice to President Biden Wed., January 20, 2021 Stephanie Arias, Evening Master’s Program, Class of 2021 Wed., January 20, 2021 More news Upcoming Events EMP Admitted Student Virtual Happy Hour Thu., January 21, 2021 | 5:30 PM A link will be sent to registered guests Chicago, IL 60637 United States Evening Master's Program Virtual Lunch Information Session Fri., January 22, 2021 | 12:00 PM A link will be sent to registered guests. Chicago, IL 60637 United States More events
Foundations of data journalism Apply now to study the foundations of evidence in data journalism. Apply Now