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I thought this reading was very interesting and had several important takeaways. This article emphasizes how occurrences of the Simpson’s paradox are higher than previously thought and how important it is to consider whether a Simpson’s paradox is present in your data. This article discusses how statistical data analysis can result in biased conclusions if you fail to consider how the relationship of data may be different for the population and subgroups, which is particularly interesting given our discussions of how data is not inherently neutral. This article hammers home the point of how important it is to consider different subgroup relationships. After reading through all of the examples of how the direction/magnitude of relationships can change, I cannot help but wonder if I’ve made causal inferences about data that would not hold up if I looked at subgroup relationships.