How Do Inclusion and Exclusion Criteria Change the Population Your Findings Apply To?

What Happens to Your Population Every Time You Exclude Someone?

Imagine that your research question concerns university instructors. You then decide that participants must be full-time instructors, have at least five years of teaching experience, be between 30 and 50 years old, teach face-to-face courses, and work in a particular academic discipline.

You may still describe the project informally as a study of “university instructors,” but that is no longer the population directly represented by your eligibility rules. Your study concerns a considerably narrower group.

This is an easily overlooked consequence of inclusion and exclusion criteria. Eligibility rules do not merely determine who gets through the recruitment door. They help determine which population your evidence directly describes and can therefore influence external validity, applicability, and the scope of your conclusions.

Every Eligibility Rule Helps Draw the Boundary of Your Evidence

How Eligibility Decisions Reshape the Population Behind Your Study

Your Population Is Not Defined by the Study Title Alone

A research title or question may begin with a broad population, but the actual study population becomes more specific as the design develops.

Suppose your research concerns “secondary-school teachers' adoption of generative AI.” Your eligibility criteria then require participants to teach in public schools, work full-time, have at least three years of experience, teach STEM subjects, and have used generative AI during the previous six months.

Those restrictions matter. The evidence now comes from a specific subset of secondary-school teachers rather than secondary-school teachers generally.

This is why the process of defining the population and choosing the sample should be treated as a connected chain. Population boundaries continue to change as eligibility, access, and recruitment decisions are made.

Eligibility Criteria Create an Eligible Population

One useful way to think about eligibility is as a filter applied to the broader population of interest.

The exact terminology used for these stages varies across disciplines, but the methodological point remains: the sample observed at the end of the process can be substantially narrower than the population named at the beginning.

Necessary Restrictions and Unnecessary Restrictions Have Different Implications

Narrow eligibility is not inherently a methodological problem.

A clinical study may need to exclude participants for safety. A qualitative study about the experience of surviving a particular event necessarily requires participants who experienced that event. A study of novice teachers may legitimately restrict eligibility by teaching experience because career stage is central to the question.

In each case, the restriction helps define the phenomenon being studied.

The concern arises when restrictions have weak connections to the research objective. Excluding older adults because they may be harder to recruit, requiring participants to speak a particular language solely because translation is inconvenient, or limiting recruitment to one demographic group without a substantive reason may produce a narrower evidence base for largely logistical reasons.

In some research settings, such exclusions also intersect with explicit policy requirements. Current NIH policy for NIH-funded clinical research, for example, expects women and members of racial and ethnic minority groups to be included unless a clear and compelling justification supports exclusion. NIH guidance also expects age exclusions in covered clinical research to be scientifically or ethically justified.

These are context-specific policy requirements, but they illustrate a broader principle: who is excluded from research can affect who is represented in the resulting evidence.

Internal Validity and External Validity Can Pull in Different Directions

Researchers sometimes restrict eligibility to create a more homogeneous study group. Under some designs, this may reduce particular sources of variation, improve safety, facilitate measurement, or help isolate an effect of interest.

But narrowing the eligible population can come with a trade-off. Findings established under tightly controlled conditions may not apply in the same way to people who differ from those permitted to participate.

The appropriate balance depends on the research question. The goal is not maximal inclusiveness regardless of design, nor maximal homogeneity regardless of applicability. It is a defensible match between the eligibility rules and the knowledge the study is intended to produce.

Excluding a Group Does Not Prove the Findings Do Not Apply to That Group

This distinction is important.

If adults older than 65 are excluded from a study, the study generally provides less direct evidence about people older than 65. That does not automatically establish that the observed relationship or intervention effect is different for them.

The correct interpretation is usually more cautious: the study did not directly establish the finding in that excluded group.

Whether the finding can reasonably be extended beyond the eligible population depends on substantive knowledge, supporting evidence, similarity of relevant mechanisms and contexts, study design, and the type of inference involved.

This is one reason discussions of generalizability, external validity, and transferability should avoid treating population boundaries as all-or-nothing.

Broad Eligibility Does Not Automatically Produce a Representative Sample

Relaxing eligibility criteria can broaden the population permitted to participate, but it does not determine who actually enters the study.

Suppose virtually every university instructor is eligible, but recruitment occurs through a voluntary invitation posted in an online community devoted to educational technology. The eligibility criteria are broad, yet the resulting sample may disproportionately attract instructors already interested in technology.

Eligibility and sampling therefore solve different problems. Eligibility criteria determine who can qualify. Sampling and recruitment determine which eligible people actually have an opportunity to become participants and which ultimately do so.

A broad eligible population combined with a highly selective recruitment process can still produce a narrow sample.

Access Can Narrow the Population Again

Even after eligibility is established, the population may contract further because not everyone eligible is realistically reachable.

For example, your criteria may permit instructors from every university in a country, but your institutional permissions may cover only five universities. In that case, the target and accessible populations differ independently of the eligibility rules.

Then sampling and nonresponse may narrow the group again.

It is therefore useful to resist describing every stage simply as “the population.” Doing so hides where restrictions enter the research process.

Restrictive Criteria Can Also Affect Equity and the Evidence Base

Eligibility decisions may have consequences beyond a single study. When certain groups are repeatedly excluded across a body of research, researchers and practitioners may have comparatively little direct evidence about whether findings apply to those groups.

This concern has been particularly visible in clinical research. NIH inclusion policies explicitly seek appropriate inclusion of women and racial and ethnic minority groups in NIH-funded clinical research, while NIH's inclusion-across-the-lifespan requirements call for scientific or ethical justification when specific age groups are excluded from covered research.

The details of those policies should not be generalized to research they do not govern. They nevertheless demonstrate why eligibility is not merely a recruitment convenience: decisions about who is allowed into research shape who is represented in accumulated knowledge.

Reporting the Criteria Allows Readers to Judge Applicability

A reader cannot assess the scope of a study if the paper does not explain who could participate.

The STROBE reporting recommendations for observational research explicitly call for authors to report participant eligibility criteria and sources and methods of selection. This information allows readers to understand the path from the population to the analyzed participants.

Rather than writing only “eligible participants were recruited,” report the characteristics that determined eligibility and explain consequential restrictions when needed. That transparency allows others to judge whether the study population resembles the population or setting in which they hope to use the findings.

Watch a Broad Population Become Narrower One Criterion at a Time

Some of those criteria might be justified under a different research question. If the study specifically concerned experienced AI-using instructors' implementation practices, for example, several restrictions would make sense.

That is precisely the point. Whether a criterion is appropriate cannot be decided independently of the question. A restriction that properly defines one population can unnecessarily distort another.

Common Mistakes About Eligibility and Population Scope

Audit What Each Criterion Does to Your Population

When drafting eligibility criteria, do not ask only, “Who qualifies?” Add a second question: “Who disappears from the population when I apply this rule?”

One practical method is to write your population definition twice: once before applying eligibility criteria and once after. If the second description is substantially narrower, decide whether that narrowing is necessary and make sure the title, methods, interpretation, and limitations do not imply a population broader than the study supports.

Before You Claim That Findings Apply Beyond Your Eligible Participants

Questions About Eligibility Criteria and Population Scope

Eligibility Rules Define the Boundaries of Your Evidence

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