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Ethical considerations for data involving human gender and sex variables

The inclusion of human sex and gender data in statistical analysis invokes multiple considerations for data collection, combination, analysis, and interpretation. These considerations are not unique to variables representing sex and gender. However, considering the relevance of the ethical practice standards for statistics and data science to sex and gender variables is timely, with results that can be applied to other sociocultural variables. Historically, human gender and sex have been categorized with a binary system. This tradition persists mainly because it is easy, and not because it produces the best scientific information. Binary classification simplifies combinations of older and newer data sets. However, this classification system eliminates the ability for respondents to articulate their gender identity, conflates gender and sex, and also obscures potentially important differences by collapsing across valid and authentic categories. This approach perpetuates historical inaccuracy, simplicity, and bias, while also limiting the information that emerges from analyses of human data. The approach also violates multiple elements in the American Statistical Association (ASA) Ethical Guidelines for Statistical Practice. Information that would be captured with a nonbinary classification could be relevant to decisions about analysis methods and to decisions based on otherwise expert statistical work. Statistical practitioners are increasingly concerned with inconsistent, uninformative, and even unethical data collection and analysis practices. This paper presents a historical introduction to the collection and analysis of human gender and sex data, offers a critique of a few common survey questioning methods based on alignment with the ASA Ethical Guidelines, and considers the scope of ethical considerations for human gender and sex data from design through analysis and interpretation.