The world is increasingly filled with data, and we are regularly bombarded with facts and figures. We must learn to analyze data and assess causal claims—a skill that is increasingly important for business and government leaders. A large body of research in behavioral economics and psychology has highlighted systematic mistakes we make when looking at data. We tend to seek evidence that confirms our preconceived notions and ignore data that might go against our hypotheses. We neglect important aspects of the way that data was generated. More broadly, it’s easy to focus on the data in front of you, even when the most important data is missing. To check yourself, a good starting place is to take the time to understand the process that is generating the data you are looking at. Rather than assuming a correlation reflects causation, ask yourself what different factors might be driving the correlation—and whether and how these might be biasing the relationship you are seeing. Let us take a closer look at your data conclusions to see how we might help address any preconceived notions.