01 · The Question
What Are You Assuming Must Be True for Your Study to Work?
Research plans are built partly from facts you have verified and partly from expectations about what will happen. You may expect enough participants to volunteer, assume that a dataset contains the variables you need, believe that an organization will approve access, or estimate that a new method can be learned before analysis begins.
Some of these assumptions can turn out to be wrong without seriously damaging the study. Others cannot.
The important feasibility question is therefore not whether your plan contains assumptions. It inevitably does. The question is: which assumption, if false, would make the study impossible as currently conceived?
02 · The Short Answer
Find the Assumption Your Study Cannot Survive
In Brief
Your critical assumption is the belief about feasibility that must be true for the study to proceed and for which being wrong would leave no realistic workaround without substantially changing the research question or design.
It might concern recruitment, access, data quality, permissions, costs, technical capability, time, or another dependency. Identifying it does not prove that the study is infeasible. It tells you where uncertainty has the greatest consequence and therefore where verification deserves priority.
03 · What You Need to Know
Feasibility Often Depends on What You Believe Will Happen
Before data collection begins, researchers necessarily make planning assumptions. You cannot know exactly how many eligible people will consent, how long every procedure will take, whether attrition will match expectations, or whether an operational process will work exactly as intended. Feasibility and pilot work exists partly because such uncertainties sometimes need empirical investigation before a larger study proceeds.
The danger arises when a consequential assumption quietly becomes treated as a fact.
Turn Your Feasibility Plan Into a Set of Testable Statements
Instead of writing “recruitment is feasible,” unpack what that conclusion requires. You might actually be assuming that the recruitment site sees enough eligible people, that enough of them can be approached, that an adequate proportion will consent, and that recruitment can be completed within your deadline.
Similarly, “the data are available” might conceal several assumptions: the dataset exists, you will be permitted to access it, the required variables are present, those variables are measured appropriately, and the usable number of observations is sufficient for the intended analysis.
Writing assumptions explicitly makes them easier to challenge. Compare these statements:
Vague planning belief
Explicit assumption
What could be checked?
Recruitment should be fine.
Approximately enough eligible participants can be recruited during the available period.
Eligibility counts, historical recruitment, screening rates, expected consent
The hospital has the data.
The accessible records contain the variables and time period required for the analysis.
Data dictionary, sample records, database documentation, data custodian
I can learn the analysis.
The required method can be learned and applied competently before the analysis deadline.
Training requirements, prerequisite skills, practice analysis, specialist advice
The organization will probably cooperate.
Required organizational permission can realistically be obtained before recruitment begins.
Formal inquiry, approval process, responsible office, expected timeline
Not Every Uncertainty Is a Critical Assumption
A research plan may contain many uncertain estimates. What makes an assumption critical is the consequence of being wrong.
Suppose you estimate that interviews will take 45 minutes. If they actually take an hour, your schedule becomes less convenient, but the study may remain entirely possible. By contrast, suppose your sampling plan assumes access to 300 eligible participants, while the accessible population turns out to contain only 40. Depending on the design and required sample, that discrepancy could undermine the study itself.
Ordinary planning assumption
An expectation that may require adjustment if reality differs from the plan.
Critical assumption
An expectation whose failure could make the intended study impossible or require a fundamental redesign.
Critical Assumptions Usually Sit Behind Critical Resources
A resource is something your study needs. An assumption is a belief about that resource or another condition of feasibility.
For example, participant access may be the resource your study cannot proceed without . Behind it may sit the assumption that enough eligible participants actually exist and can be recruited within the project period.
The distinction matters because verifying that a resource exists does not necessarily verify the assumptions surrounding it. A laboratory may possess the required instrument, yet your plan may still assume that it will be available every week for six months. A collaborator may have the necessary expertise, yet your feasibility plan may assume that the person has enough time to complete every required analysis.
Use the “What If I Am Wrong?” Test
For each major feasibility assumption, ask what happens if reality is substantially worse than expected.
If you assume a 40% recruitment rate, what happens at 20%? If you expect approval in four weeks, what happens if it takes four months? If you assume a dataset contains a key variable, what happens if that variable was never collected? If you expect to learn a statistical technique in one semester, what happens if competent application requires considerably more training?
This is not an invitation to invent catastrophic scenarios. The point is to examine plausible departures from your expectations and identify which ones your design cannot absorb.
Separate Probability From Consequence
Two questions should be considered separately:
How likely is the assumption to be wrong?
How damaging would it be if it were wrong?
An assumption can deserve verification even when failure seems unlikely if the consequence would be severe. Conversely, an uncertain assumption may deserve less attention when the study can easily adapt.
Likelihood of being wrong
Consequence if wrong
Practical concern
Low
Low
Usually manageable through ordinary planning
High
Low
Plan flexibility or a simple contingency
Low
High
Verify because failure would be consequential
High
High
Major feasibility risk requiring early attention
Some Assumptions Are Empirical; Others Require Confirmation
The appropriate verification method depends on what you are assuming. A claim about participant availability might be checked against records from the recruitment setting. A belief about data content might be checked using a data dictionary or preliminary data inspection. Equipment availability might require confirmation from the facility manager. Methodological capability may require consultation with someone who understands the analysis.
Some uncertainty is best addressed through a small feasibility check before the full study . Pilot and feasibility studies can examine uncertainties such as recruitment, data collection procedures, intervention delivery, acceptability, and operational processes before researchers invest in a larger project.
The Assumption May Be About You
Researchers understandably focus on external barriers, but the critical assumption can concern their own capacity. “I will learn the method later” is still a feasibility assumption.
If a project depends on an unfamiliar analytical technique, the relevant question is not merely whether the method can theoretically be learned. You need to consider whether it can be learned to an appropriate level within the available timeline. The distinction between learning a new method and choosing a question that fits your current skills becomes consequential when the entire design depends on that capability.
Feasibility Assumptions Should Become More Evidence-Based as Commitment Increases
Early research ideas can reasonably contain substantial uncertainty. You may initially be exploring possibilities rather than making commitments. As the project moves toward protocol development, ethics submission, funding, registration, or thesis approval, however, consequential assumptions should increasingly be supported by evidence.
This is the difference between recognizing uncertainty and building a project on optimism. A sound feasibility assessment does not require certainty about everything. It does require proportionate evidence for assumptions that could determine whether the study survives.
04 · A Practical Example
One Plausible Assumption Can Quietly Determine the Whole Study
Hypothetical Example
A survey requiring a difficult-to-reach professional population
A graduate researcher plans a survey requiring 250 respondents from a specialized professional group. A professional organization has agreed in principle to distribute the survey link to its members, so the researcher initially considers recruitment feasible.
State the hidden assumption The plan assumes that distribution through the organization will produce enough eligible respondents to reach the required sample within the data-collection period.
Ask what happens if it is wrong The organization may distribute the invitation successfully while only a small proportion of members respond. Access to a mailing list is not equivalent to successful recruitment.
Examine the consequence If only 60 eligible participants respond and no alternative recruitment pathway exists, simply extending the survey for a few days may not solve the problem.
Gather evidence The researcher asks whether comparable surveys have previously been distributed, how many eligible members can actually be reached, and what response levels have historically been observed.
Reassess feasibility The research design can now be evaluated using evidence about the recruitment pathway rather than the assumption that organizational access will automatically produce the required sample.
The lesson is not that surveys of specialized populations are inherently infeasible. It is that a seemingly reasonable statement such as “we have access to participants” may conceal the assumption that actually determines whether the project can be completed.
06 · What This Means for You
Put Your Most Consequential Assumption Under the Most Scrutiny
Take your current research plan and complete a simple sentence: “This study is feasible only if...”
You may produce several answers. That is fine. Now ask which statement would cause the greatest damage to the study if it proved false.
A simple decision framework
If the assumption is uncertain but failure would cause little disruption
Document it and retain enough flexibility to adjust.
If the assumption is uncertain and failure would require substantial redesign
Seek stronger evidence before committing to the design.
If the assumption appears likely to be true but failure would make the study impossible
Verify it where reasonably possible because the consequence remains high.
If the assumption is both poorly supported and critical to the study
Treat it as a major feasibility risk rather than quietly building it into the protocol.
This exercise also helps reveal whether your feasibility plan is based on evidence or optimism . Confidence is useful when designing research, but it is not a substitute for information about the few conditions on which the project genuinely depends.
07 · A Quick Checklist
Before Relying on a Critical Assumption, Check the Evidence
Before committing to the research design, check:
Write down the major assumptions underlying recruitment, access, data, methods, resources, costs, and timelines.
Rewrite vague expectations as explicit statements that could, at least in principle, be checked.
Ask what would happen to the study if each assumption were substantially wrong.
Identify assumptions whose failure would require a fundamental change to the question, population, method, or evidence.
Separate the likelihood that an assumption is wrong from the consequence if it is wrong.
Look for existing evidence such as administrative records, previous recruitment figures, data dictionaries, documented timelines, or technical specifications.
Seek formal confirmation when the assumption concerns access, permission, equipment, services, or external cooperation.
Use a targeted feasibility check when the uncertainty can only be resolved empirically.
Reconsider the design if the study still depends on a consequential assumption for which there is little supporting evidence.
09 · The Bottom Line
Find the Belief Your Research Plan Cannot Afford to Get Wrong
The Bottom Line
Your critical assumption is the feasibility belief that must hold for your study to work and whose failure would make the intended study impossible or force a fundamental redesign.
Make that assumption explicit, examine both its uncertainty and its consequence, and seek evidence proportionate to how much depends on it. You do not need certainty about every detail before beginning research, but the assumptions capable of stopping the project deserve more than optimism.
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.
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