  The measurement and analysis of human sex and gender is a nuanced problem
with many overlapping considerations including statistical bias, data privacy,
and the ethical treatment of study subjects. Traditionally, human gender and
sex have been categorized and measured with respect to an artificial binary
system. The continuation of this tradition persists mainly because it is easy
to replication and not, as we argue, because it produces the most valuable
scientific information. Sex and gender identity data is crucial for many
applications of statistical analysis and many modern scientists acknowledge the
limitations of the current system. However, discrimination against sex and
gender minorities poses very real privacy concerns when collecting and
distributing gender and sex data. As such, extra thoughtfulness and care is
essential to design safe and informative scientific studies. In this paper, we
present statistically informed recommendations for the data collection and
analysis of human subjects that not only respect each individual's identity and
protect their privacy, but also establish standards for collecting higher
quality data.
