01 · The Question
What Evidence Supports Your Claim That the Study Is Feasible?
“I think I can recruit enough participants.” “The organization will probably approve access.” “I should be able to learn the analysis.” “The project should fit within one semester.”
None of those statements is necessarily wrong. Early research planning inevitably involves estimates and uncertainty. The problem begins when plausible expectations quietly become the evidence for feasibility.
A useful feasibility plan does more than describe how you hope the study will work. It asks what you actually know about the conditions required to complete it, where that knowledge comes from, how uncertain it remains, and what happens if reality differs from your expectations.
03 · What You Need to Know
Feasibility Is a Claim That Should Be Justified
Feasibility is commonly included among the criteria for evaluating research questions. The FINER framework, for example, asks whether a question is feasible alongside whether it is interesting, novel, ethical, and relevant. Feasibility may involve the availability of participants or data, technical expertise, funding, time, institutional support, and other resources needed to complete the work.
That makes “this study is feasible” a substantive claim. Like other consequential claims in research, it should have a basis.
Start by Separating Evidence From Assumptions
Consider a researcher planning to recruit 150 participants in three months. The statement “150 participants can be recruited in three months” may be supported by several very different kinds of information.
Basis for the claim
What it tells you
How much confidence should it provide?
“I think people will participate.”
The researcher expects recruitment to succeed.
Very limited evidence
The site has 2,000 potentially eligible people.
A large population may be available.
Useful, but eligibility and participation remain uncertain
Administrative records show approximately 180 eligible people encounter the service each month.
The potential recruitment pool is better characterized.
Stronger evidence about availability
A comparable study at the same site recruited about 55 participants per month using a similar procedure.
Actual recruitment performance under related conditions is known.
More directly relevant evidence
A small feasibility study tests screening, consent, and recruitment procedures.
The proposed process is observed under conditions resembling the planned study.
Potentially strong evidence for the specific uncertainty tested
Evidence does not eliminate uncertainty. Even historical recruitment performance cannot guarantee future recruitment. What changes is the basis on which the estimate is made.
Different Feasibility Claims Require Different Evidence
There is no single feasibility test. A claim about recruitment should not be justified in the same way as a claim about software access or methodological competence.
If you are estimating recruitment, relevant evidence might include the number of eligible people encountered at the proposed sites, previous recruitment performance, screening rates, expected consent, and anticipated attrition. If you are estimating costs, quotations, published fees, institutional charges, licensing information, travel estimates, and participant-payment requirements may be more useful.
If your study depends on equipment, the evidence might concern specifications, booking availability, maintenance schedules, fees, and authorized access. If it depends on an external dataset, you may need documentation about variables, coverage, access procedures, eligibility, and expected approval timelines.
This is why a generic sentence such as “the institution has adequate resources” is rarely enough for serious feasibility planning. The evidence should correspond to what the study actually requires.
Pay Particular Attention to the Assumptions That Could Stop the Study
You do not need to investigate every minor estimate with equal intensity. The priority should be assumptions with large consequences.
If interviews take 55 minutes instead of the estimated 45, the study may simply require some scheduling adjustments. If your entire design assumes access to a dataset that turns out not to contain the primary outcome, the consequences are considerably greater.
Identifying the assumption that could make the study impossible helps determine where your strongest feasibility evidence is needed.
Optimism Often Hides in Particular Words
Language can reveal when a feasibility claim has not yet been investigated. Words such as “probably,” “hopefully,” “should,” “likely,” and “I think” are not inherently problematic. They become important signals when they appear beside a critical dependency.
Consider the difference:
Optimistic planning
“The laboratory should be available because our department uses it regularly.”
Evidence-based planning
“The laboratory manager confirmed that the required instrument is suitable for the procedure, explained the booking process, and identified the periods when access is normally available.”
The second statement still does not guarantee access. It does, however, make clear what has been checked and what remains uncertain.
Evidence Quality Depends on How Closely It Matches Your Situation
Not all evidence deserves equal weight. Information becomes more useful when it closely matches the resource, population, procedure, setting, and timeline of the proposed study.
A published paper reporting successful recruitment at a large specialist hospital may provide useful background, but it may say little about recruitment at your smaller site. A supervisor's experience with a similar method can inform planning, but current confirmation from the facility controlling the resource may be more relevant for equipment availability.
Ask three questions about feasibility evidence: Is it relevant to this study? Is it sufficiently current? Does it directly address the uncertainty I am trying to resolve?
Document What You Know, What You Estimate, and What You Do Not Yet Know
One of the simplest ways to improve a feasibility plan is to separate verified information from estimates and unresolved questions.
Feasibility issue
Current evidence
Remaining uncertainty
Next check
Participant availability
Site records indicate approximately 90 potentially eligible cases monthly.
Actual consent rate is unknown.
Review comparable recruitment or test recruitment procedures.
Software
Institutional license confirmed through the project period.
None material currently identified.
No immediate action.
Data access
Data custodian confirmed that an application pathway exists.
Approval has not yet been granted.
Confirm requirements and likely processing time.
Analysis skills
Researcher has completed introductory training.
Competence for the planned advanced model is uncertain.
Consult a methodologist and assess training requirements.
This kind of record prevents uncertainty from disappearing simply because the proposal has progressed. Academic paperwork has many talents; making an unsupported assumption true is not among them.
Use Pilot or Feasibility Work When the Important Uncertainty Must Be Tested Empirically
Some feasibility questions cannot be answered adequately from documents or conversations. You may need to observe whether recruitment works, whether participants tolerate a procedure, whether data can be collected as intended, or whether study processes function together.
Pilot and feasibility studies are specifically used to examine uncertainties relevant to whether and how a larger study should proceed. Their objectives should focus on feasibility rather than being treated merely as underpowered versions of the eventual definitive study.
When uncertainty is consequential and empirical, a small feasibility check before the full study may provide more useful evidence than increasingly elaborate speculation.
Evidence-Based Does Not Mean Risk-Free
No amount of planning can guarantee that recruitment will proceed exactly as expected, an external partner will never withdraw, equipment will never fail, or a project will encounter no delay.
The purpose of evidence-based feasibility planning is therefore not prediction with certainty. It is to make the decision to proceed defensible given what can reasonably be known beforehand.
A plan can contain unresolved risks and still be well founded if those risks are recognized, their consequences are understood, and contingencies are considered where appropriate. Conversely, a detailed plan can remain weak if its crucial numbers and access assumptions are merely precise-looking guesses.
04 · A Practical Example
Turning “Recruitment Should Be Fine” Into a Feasibility Assessment
Hypothetical Example
A student planning six months of recruitment
A researcher needs 120 eligible participants and has six months for recruitment. The proposed site serves many people, so the initial plan states that obtaining the sample should be feasible.
Expose the assumption Recruiting 120 participants in six months requires an average of 20 enrolled participants per month. The researcher has not yet established whether the site can realistically produce that number.
Gather relevant evidence Site records indicate that approximately 70 people who might meet the broad eligibility criteria are seen each month. More detailed screening suggests that around 40 are likely to meet the study criteria.
Add participation uncertainty If roughly half of eligible people ultimately consent and complete enrollment, the expected recruitment rate would be around 20 per month. That would place the study close to its required pace rather than comfortably above it.
Stress-test the estimate The researcher considers what happens if eligibility or consent is lower than expected and investigates whether another recruitment pathway or additional time would be available.
Revise the feasibility conclusion Instead of writing “recruitment should be easy,” the researcher can describe the evidence supporting the estimate, identify the assumptions that remain, and recognize that recruitment is feasible but potentially vulnerable to modest underperformance.
The calculation is not a guarantee. Its value lies in making the reasoning visible. The researcher now knows what must happen each month, which parts of the estimate are supported by evidence, and where the project remains exposed.
06 · What This Means for You
Audit the Claims Behind Your Feasibility Plan
Take each major statement in your feasibility plan and ask a simple question: How do I know this?
If the answer is a record, quotation, documented policy, prior recruitment figure, technical specification, preliminary analysis, relevant previous experience, formal confirmation, or feasibility test, you have something that can be evaluated. If the answer is mainly “I think,” “probably,” or “we will figure it out,” you have identified an assumption that may need attention.
A simple decision framework
If the claim is well supported and failure would have little consequence
Ordinary planning may be sufficient.
If the claim is weakly supported but easy to verify
Verify it before relying on it.
If evidence is limited and failure would substantially disrupt the study
Seek stronger evidence, develop a contingency, or modify the design.
If a critical claim cannot be resolved without trying the process
Consider a focused feasibility or pilot assessment before full commitment.
Pay particular attention to the resources you have not yet verified before committing to the research question . Feasibility problems are much easier to manage while the design can still change.
07 · A Quick Checklist
Check Whether Your Feasibility Plan Has an Evidence Base
Before treating the study as feasible, check:
Write down the major claims you are making about recruitment, time, costs, access, resources, expertise, and study procedures.
For each consequential claim, identify the information on which it is based.
Separate verified facts from estimates, expectations, and unresolved assumptions.
Check whether your evidence comes from conditions sufficiently similar to those of the proposed study.
Use current information when access, costs, staffing, policies, or resource availability may have changed.
Stress-test important estimates by considering plausible conditions that are less favorable than expected.
Give the strongest scrutiny to assumptions whose failure could stop the study or force major redesign.
State important residual uncertainty explicitly instead of presenting estimates as guarantees.
Use a targeted feasibility check when documentary evidence cannot resolve a consequential empirical uncertainty.
11 · Cite this Guide
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.
Recommended (Field Guide)
APA
MLA
Chicago
Copy Citation
