Which Populations Are Missing From the Existing Literature?

Whose Evidence Is Missing From the Literature?

After mapping who appears repeatedly in a body of research, the inverse question becomes difficult to ignore: who does not appear?

Perhaps most studies involve younger adults while older adults are scarcely represented. A literature on educational technology may concentrate on university students while evidence from primary schools remains thin. Research may span several countries but almost entirely represent urban institutions. Certain occupations, socioeconomic circumstances, disability groups, clinical populations, or institutional settings may rarely appear.

Identifying these absences can reveal consequential limits in an evidence base. Yet a missing population is not automatically a research gap. The important issue is whether the absence prevents researchers from answering a meaningful question, evaluating variation, or applying existing knowledge with appropriate confidence.

Compare Who Has Been Studied With Who the Evidence Needs to Represent

How to Identify Meaningful Population Gaps

Begin With the Population the Research Is Supposed to Inform

You cannot determine who is missing until you know who ought to be represented.

That reference population depends on the research question. If a study concerns a condition that affects people across the lifespan, excluding older adults may matter considerably. If the question concerns first-year university adjustment, the absence of retirees is irrelevant. Representation should therefore be evaluated against the intended inference, not against a generic expectation that every study must contain every possible group.

This principle is explicit in some research policies. NIH's Inclusion Across the Lifespan policy requires NIH-supported human-subjects research to include participants across ages unless scientific or ethical reasons justify exclusion, with the stated purpose of helping ensure that resulting knowledge is applicable to people affected by the researched conditions. NIH also requires appropriate inclusion of women and members of racial and ethnic minority groups in NIH-funded clinical research, subject to scientifically or ethically justified exclusions.

Those requirements apply to particular NIH-funded research and should not be treated as universal rules for every discipline. They nevertheless illustrate the underlying methodological issue: participant composition should fit the scientific question and the population to which conclusions are intended to apply.

Map Absence Relative to Existing Concentration

A useful population-gap analysis begins by identifying which populations dominate the literature. Missing groups often become visible only in comparison with those concentrations.

Suppose 80 studies examine an intervention. Sixty-five involve adults aged 18 to 35, twelve involve middle-aged adults, and three include older adults. The issue is not merely that younger adults appear frequently. It is that the evidence available for older adults may be insufficient relative to the claims researchers wish to make.

Population representation can be classified more carefully than simply “present” or “absent.”

Presence Is Not the Same as Adequate Representation

A population can technically appear in a study while contributing little useful evidence about that group.

Imagine a sample of 2,000 participants containing 30 members of a population relevant to your question. Those participants are not absent, but the study may have limited ability to estimate group-specific effects or examine meaningful heterogeneity. Similarly, researchers may report broad demographic categories that conceal important differences within them.

For this reason, do not reduce population mapping to a checkbox. Ask whether the available evidence can actually support the inference you need.

Look Beyond Demographic Categories

Missing populations are often discussed in terms of age, sex, race, or ethnicity, but population gaps can involve many other characteristics.

Depending on the research problem, meaningful dimensions might include:

• geographic region or country;
• urban, rural, or remote settings;
• educational level or institution type;
• occupation or employment arrangement;
• socioeconomic circumstances;
• disability status or accessibility needs;
• clinical condition or disease severity;
• language;
• technology access;
• organizational size or sector; or
• other characteristics with a plausible relationship to the phenomenon.

The relevant categories should emerge from the question, theory, prior evidence, and intended use of the findings rather than from an indiscriminate demographic inventory.

Geographic Representation Requires More Than Counting Countries

A map showing ten countries can look geographically diverse while still representing a narrow set of contexts. Perhaps nine are high-income countries. Perhaps every sample comes from metropolitan universities. Perhaps studies in lower-resource settings use different measures and cannot be compared directly.

When geography matters, examine the contextual features that make geographic variation scientifically relevant. Country labels can serve as useful descriptors, but they should not become crude substitutes for culture, institutions, resources, policy environments, or other mechanisms that might influence the phenomenon.

Ask Whether There Is a Reason to Expect the Finding Could Differ

This is one of the most important tests of a population gap.

If a group is missing, ask why inclusion could change what researchers know. Perhaps baseline risk differs. Perhaps access to resources changes the intervention's feasibility. Perhaps developmental stage matters. Perhaps institutional conditions alter implementation. Perhaps an instrument has not been validated for the population. Perhaps the population bears much of the real-world burden associated with the problem.

You do not need evidence that the result definitely will differ. If that evidence already existed, the question would be less open. But you should articulate a credible theoretical, empirical, practical, or equity-related reason why direct evidence would be informative.

Do Not Assume Differences Merely Because Populations Are Different

The opposite error is also possible. Researchers can overstate population gaps by assuming that every demographic or geographic distinction must produce different findings.

Population-specific research is strongest when the proposed distinction is connected to a plausible mechanism, important application, known inequity, theoretical prediction, policy requirement, or uncertainty in generalization.

This avoids a weak form of gap construction: “Study X has been conducted in Country A but not Country B, therefore it should be repeated in Country B.” Location alone does not explain what the replication would teach us.

Distinguish Missing Populations From Barely Studied Questions

A population gap and a question-level gap overlap, but they are not identical.

The central question may be well studied overall while being poorly studied in one relevant population. Conversely, a question may be barely addressed across every population because the entire evidence base is immature.

This distinction matters for your contribution. In the first situation, your study may test the boundary or transferability of an established finding. In the second, it may contribute to building the basic evidence base itself.

Check Whether the Missing Population Is Also Linked to Missing Methods or Outcomes

Population gaps rarely exist in isolation. The few studies involving an underrepresented group may also use a narrow set of methods or outcomes.

For example, a population might appear frequently in qualitative research but rarely in intervention studies. Another may appear in administrative datasets but seldom in research measuring lived experience. Simply counting participants could conceal these asymmetries.

Cross-reference population coverage with methods that are missing and outcomes that are neglected. The most informative gap may lie at their intersection.

Verify Apparent Absence Before Claiming It

Statements such as “no studies have examined this population” require a strong search foundation. Relevant research may use terminology you did not search, appear in regional databases, be published in another language, sit within a neighboring discipline, or describe the population using different categories.

Scoping-review methodology is particularly useful when the purpose is to map the extent and characteristics of evidence. A transparent search strategy and explicit eligibility criteria make claims about population coverage much more defensible than an informal literature search.

Underrepresentation Can Limit the Claims the Literature Supports

The most consequential population gaps affect inference.

In clinical research, for example, NIH states that inclusion policies are intended to help produce findings applicable to populations affected by the conditions under investigation. The logic extends beyond that policy context: if a consequential group is inadequately represented, uncertainty may remain about whether effects, associations, experiences, or implementation patterns observed elsewhere apply similarly to that group.

That does not make the existing studies invalid. It defines the boundary of what they can reasonably tell you.

Turning an Underrepresented Population Into a Meaningful Question

Common Mistakes When Identifying Missing Populations

Move From “Who Is Missing?” to “Why Does Their Absence Matter?”

A population gap becomes useful when you can connect representation to inference. Begin with the evidence distribution, then identify what researchers cannot confidently conclude because a relevant population is missing or poorly represented.

The strongest formulation usually follows a simple logic: this population matters to the question, existing evidence does not adequately represent it, and therefore a specific inference remains uncertain.

Before Claiming That a Population Is Missing

Questions About Missing and Underrepresented Research Populations

A Missing Population Matters When Its Absence Leaves an Important Inference Uncertain

How to Cite This Guide

This guide is intended to be read, shared, and used in research, teaching, and academic work. If you draw on its ideas, explanations, or other content, please acknowledge the source by citing the guide. Doing so gives appropriate credit and helps your readers locate the original resource.