Summary:
- The article provides a statistical and methodological critique of how researchers handle the variables of "sex" and "gender" in empirical studies, arguing that conflating the two or treating them as interchangeable proxies leads to flawed analytical outcomes.
- It emphasizes the importance of measurement validity in data science, asserting that treating social constructs and biological categories as identical without rigorous justification constitutes a failure of scientific practice rather than a mere semantic disagreement.