  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.
