There is no single correct way to categorize age groups in research. The right approach depends on your field, your research question, and which standards your audience or funding body expects. What matters most is choosing age bins that are specific, justified, and consistent with how other researchers in your area report their data. This guide walks through the major frameworks, from international health standards to style guide requirements, so you can pick the one that fits your study.
The 5-Year Bin: The Most Common Starting Point
Five-year age intervals are the default in most health and demographic research. Both the World Health Organization and the U.S. Census Bureau use them: 0–4, 5–9, 10–14, 15–19, and so on up through the oldest ages. The WHO’s World Standard Population extends these bins all the way to 100+, while earlier standards stopped at 85+. The Census Bureau follows the same pattern.
The reason five-year bins are so widely adopted is practical. They’re narrow enough to reveal meaningful patterns in disease rates, mortality, and behavior, but wide enough that most studies can fill each bin with a reasonable number of participants. A call for standardized reporting in The Lancet Healthy Longevity recommended five-year groups for all health data, with one exception: children under five, where rapid biological changes justify finer disaggregation (separating infants under 1 year from toddlers, for instance).
If you’re unsure where to start, five-year bins aligned with the WHO or Census standards give you built-in comparability with national and global datasets. That alone is a strong reason to use them.
Pediatric Age Categories
Research involving children and adolescents typically requires more granular categories than a simple five-year split. The FDA defines pediatric subpopulations with specific cutoffs used in clinical trials and device approvals:
- Neonates: birth through the first 28 days of life
- Infants: 29 days to less than 2 years
- Children: 2 years to less than 12 years
- Adolescents: 12 through 21 years
That upper boundary of 21 surprises many people. The Federal Food, Drug, and Cosmetic Act defines pediatric patients as persons aged 21 or younger at the time of diagnosis or treatment, which is older than the legal definition of adulthood in most contexts. The NIH, by contrast, defines a child as anyone under 18 for the purposes of its inclusion policies.
These differences matter. If your study involves participants in the 18–21 range, you may need to clarify which definition you’re following and why. In cancer research, infants under 1 year are often separated out entirely because infant cancers can have prenatal origins, making them biologically distinct from cancers in older children.
Older Adult Subcategories
Lumping everyone over 65 into a single “elderly” category is one of the most common mistakes in age group design. A 66-year-old and a 92-year-old differ enormously in health status, mobility, cognitive function, and medication use. Gerontology research typically splits older adults into three tiers:
- Young-old: 65 to 74 years
- Middle-old: 75 to 84 years
- Oldest-old: 85 years and above
These categories appear consistently across clinical and emergency medicine literature. A study in Clinical and Experimental Emergency Medicine used exactly these breakpoints and found meaningful differences among the three groups in how they presented and what outcomes they experienced. If your research touches aging populations, these subcategories will give you far more useful data than a single 65+ bin.
Generational Labels: When They Apply
Generational categories like Gen Z (born 1997–2012) or Generation Alpha (born roughly 2011–2025) are not age groups in the scientific sense. They describe birth cohorts shaped by shared cultural and historical experiences. APA Style guidelines are explicit on this point: generational descriptors like “baby boomers,” “millennials,” or “Gen Z” should only be used when the study is specifically about generational differences or generational identity.
Using generational labels as a substitute for age ranges creates problems. The boundaries are somewhat arbitrary, different organizations define them differently, and the categories shift over time as cohort members age. If your research question is about age-related biology, health, or behavior rather than generational culture, use numeric age bins instead.
What Style Guides Require
Both APA and AMA style have specific expectations for how you describe and label age groups in your writing. The core rules overlap significantly.
APA Style (7th edition) asks researchers to be as specific as possible on first reference to any age group by including the age range, average age, and median age when available. Open-ended categories like “under 18” or “over 65” should be avoided unless you’re describing broad eligibility criteria. For terminology, “older adults,” “older persons,” and “older people” are preferred over “elderly,” “seniors,” or “the aged,” which APA considers stigmatizing even if participants use those terms themselves.
For younger populations, APA recommends “adolescent” or “young person” for ages 13–17, and “adult” for anyone 18 and older. AMA style follows nearly identical boundaries: “child” for ages 1–12, “adolescent” for 13–17, and “adult” for 18 and above. Both guides encourage using the terms individuals use to describe themselves when it comes to gendered age labels.
You can also use decade-specific descriptors like “octogenarian” or “centenarian” if they fit your context, though numeric ranges are almost always clearer.
How to Choose the Right Bins for Your Study
Your age categories should be driven by your research question, not by convenience. Here are the practical considerations that matter most.
Match Your Bins to the Biology or Behavior
If smoking rates, disease incidence, or the outcome you’re measuring changes sharply at certain ages, your bins should capture those transitions. Grouping 18-year-olds with 32-year-olds, for example, can mask major differences in smoking behavior. The whole point of categorization is to reveal patterns, so your bins need to be narrow enough to do that.
Align With Existing Standards When Possible
Using WHO five-year bins, FDA pediatric categories, or established gerontology tiers lets you compare your findings directly against other published work. If you invent custom categories, you’ll need to justify them and your readers will have a harder time contextualizing your results.
Check Your Funder’s Requirements
The NIH requires that all human subjects research include participants across the lifespan unless there’s a scientific or ethical reason to exclude certain ages. Researchers must report age at enrollment in progress reports, down to units as fine as minutes for neonatal studies. If you’re applying for NIH funding, build your age reporting plan into the study design from the start.
Avoid Bins That Are Too Wide
Broad categories are the most frequent problem. A bin spanning 20 years (like 40–59) can hide the very trends your study is trying to detect. When in doubt, collect age as a continuous variable and bin it during analysis. This gives you the flexibility to test different groupings and report the one that best fits your data, as long as you’re transparent about the process.
The strongest approach is often the simplest: report exact ages as continuous data in your analysis, present results in standard five-year bins for comparability, and use labeled subcategories (like young-old or adolescent) only where they add clarity for your specific audience.