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.
翻译:人类性与性别的衡量和分析是一个细微问题,有许多相互重叠的考虑,包括统计偏见、数据隐私和研究科目的道德处理。传统上,人类性别和性别被归类,并用人工二元系统衡量。这一传统之所以继续存在,主要是因为易于复制,而不是如我们所认为的那样,因为它产生最宝贵的科学信息。性和性别认同数据对于统计分析的许多应用至关重要,许多现代科学家承认现有系统的局限性。然而,在收集和传播性别与性别数据时,对性和性别少数群体的歧视是真实的隐私问题。因此,对设计安全和内容丰富的科学研究,特别的思考和谨慎至关重要。在本文件中,我们为收集和分析不仅尊重每个人的身份和保护他们的隐私,而且为收集更高质量的数据制定标准的人的数据,提出了具有统计依据性的建议。