03 · What You Need to Know
The Distinction Begins With the Construct You Want to Study
Researchers sometimes inherit a variable labeled “sex/gender” from an existing dataset or add a demographic question because it is customary. Neither approach establishes what the variable actually measures.
A stronger approach begins with the construct. Ask why sex, gender, or both could matter to the phenomenon. Only then should you decide what information needs to be collected.
Sex Generally Refers to Biological Attributes
In biomedical research, sex is generally used to describe biological attributes associated with females and males. Depending on the research question, relevant attributes can involve chromosomes, reproductive anatomy, hormones, physiology, or other biological characteristics.
Not every study needs to measure all of these attributes. In many human studies, researchers use a recorded or self-reported sex variable because that level of information is sufficient for the research purpose. In other studies, a specific biological characteristic may be the actual variable of interest.
This distinction matters because a categorical sex variable is not a direct measurement of every biological mechanism that might differ among participants. If the hypothesis concerns a particular hormone, anatomy, reproductive process, or other biological mechanism, researchers should consider whether that characteristic needs to be measured more directly.
Gender Generally Refers to Socially and Culturally Shaped Dimensions
Gender is generally used in research to address socially and culturally shaped identities, roles, behaviors, expressions, relations, and expectations. Depending on the research question, a researcher might be interested in gender identity, gender roles, gender norms, social treatment associated with gender, or another specific dimension.
These are not necessarily captured by asking participants to select a single category labeled “gender.” If the proposed explanation concerns caregiving expectations, occupational roles, discrimination, social norms, or another gender-related mechanism, measuring that mechanism may provide more useful evidence than relying on a broad demographic category alone.
Sex
Generally concerns biological attributes relevant to the research question.
Gender
Generally concerns socially and culturally shaped identities, roles, behaviors, expressions, relations, and expectations.
The Distinction Changes What You Measure
Suppose researchers are investigating whether a medication is metabolized differently among participants. Biological characteristics may be central to the hypothesis. Now suppose researchers are investigating differences in willingness to seek mental-health support. Social norms, identities, expectations, access, and experiences may be highly relevant.
Simply inserting “male/female” into both datasets does not make the same variable equally informative in both studies.
The measurement should correspond to the mechanism or descriptive characteristic the research actually needs. This is a broader principle of research design: demographic labels should not substitute for conceptual reasoning.
Sex and Gender Can Both Influence the Same Outcome
In human research, biological and social influences do not conveniently occur in separate laboratories. Health, education, employment, behavior, technology use, and many other outcomes may reflect interactions among biological characteristics, gender-related experiences, social conditions, environmental exposures, and other factors.
An observed difference between groups therefore does not reveal its own cause.
If two groups differ in an outcome, that difference might reflect biological processes, gender-related experiences, another correlated variable, or several influences operating together. Researchers should distinguish describing a difference from explaining it.
Watch Out
Finding a statistical difference between groups classified by sex does not prove that the difference is biologically caused. Likewise, a difference between gender groups does not by itself identify the social mechanism responsible. Causal explanations require evidence beyond the group label.
Terminology Has Not Been Consistent Across Research
Sex and gender have often been used interchangeably in scholarly literature, questionnaires, administrative records, and policy documents. Researchers working with historical or secondary data should therefore not assume that a variable's label tells them exactly how it was collected.
If a dataset contains “gender: male/female,” inspect the original instrument, codebook, data dictionary, or documentation. Determine what participants were actually asked and how responses were recorded. When the underlying measurement cannot be established, acknowledge the ambiguity rather than silently relabeling the variable.
Terminology may also differ among disciplines, jurisdictions, institutions, funders, journals, and regulatory bodies. Current requirements should be checked directly when they apply to a particular project.
Policies May Define and Use These Terms Differently
Researchers should be especially cautious about presenting one organization's terminology as a universal research standard.
For example, the U.S. National Institutes of Health currently requires consideration of sex as a biological variable in applicable NIH-funded vertebrate animal and human research. NIH states that sex should be factored into research design, analysis, and reporting, with strong justification required for applicable studies proposing only one sex.
NIH's current inclusion policy for women and racial and ethnic minority groups in clinical research was revised in 2025 and uses “sex” rather than the earlier “sex/gender” terminology. This is a specific NIH policy context, not a universal definition governing every research discipline or jurisdiction.
Researchers should therefore verify the terminology and reporting requirements of their own funder, ethics body, regulator, journal, and discipline rather than assuming that one policy settles the conceptual question everywhere.
Reporting Guidelines May Ask Researchers to Address Both
The Sex and Gender Equity in Research (SAGER) guidelines provide recommendations for reporting sex and gender information in research and encourage authors to use the terms carefully rather than interchangeably. The guidelines apply across sections of a research report and are particularly useful when sex or gender may affect the research question, design, analysis, or interpretation.
Reporting guidance does not mean that every study must perform every conceivable sex- or gender-based analysis. Rather, researchers should explain how these variables were considered when relevant and avoid terminology that obscures what was actually studied.
Categories Can Conceal Variation Within Them
Neither sex nor gender should be treated as though a category completely describes the people assigned to it. Individuals within the same category can differ substantially in biology, identity, experience, environment, behavior, socioeconomic conditions, and numerous other characteristics.
This is part of the broader problem of how demographic categories can oversimplify the people being studied.
Categories can be analytically useful. The methodological responsibility is to avoid asking them to explain more than they actually measure.
04 · A Practical Example
How Confusing Sex and Gender Can Change the Interpretation
Hypothetical Example
Studying differences in use of an online mental-health service
Researchers examine use of a university mental-health platform. Their questionnaire records participants as women or men under a field labeled “gender.” They find that women in the sample use the service more frequently.
Observation The recorded groups differ in their average use of the service.
Unsupported interpretation The researchers conclude that biological sex causes the difference in help-seeking behavior.
Measurement problem The study did not measure a biological mechanism and provides little information about gender norms, stigma, prior experience, access, or other possible explanations.
Defensible conclusion The researchers can report the observed difference using terminology consistent with what their instrument actually measured, while avoiding an unsupported causal explanation.
Better future design If the researchers want to explain the difference, they can measure the specific biological, social, behavioral, or contextual factors implicated by their hypotheses.
The difference in the dataset may be real. What changes is the strength of the explanation. A group comparison tells researchers that groups differed under the study conditions; it does not automatically tell them why.
