# Feasibility in Specific Aims ## Quick Answer - Reviewers judge feasibility by whether your preliminary data, methods, timeline, and budget align with each stated aim, so build each aim around evidence you already hold. - Start by mapping every aim to a specific method, a realistic time window, and a cost line item before you write the grant narrative. - Feasibility is never guaranteed by enthusiasm alone, and a well written aim can still fail if your institution lacks the equipment, animal access, or personnel you describe. ## Understanding What Feasibility Means in Grant Review Feasibility in a grant application is the reviewers' assessment of whether you can realistically complete the proposed work within the funding period, with the resources available, and using the methods you describe. It is not a measure of how important the question is or how clever the hypothesis appears. Reviewers look for a coherent chain that connects each specific aim to a concrete experimental plan, a defensible timeline, and a budget that matches the work. The National Institutes of Health grant process expects applicants to describe their research strategy in a way that lets reviewers evaluate scientific merit and the likelihood of successful completion. When you write specific aims, you are making a promise about what you will deliver. Feasibility is the evidence that this promise can be kept. Reviewers will ask themselves whether the preliminary data support the approach, whether the methods are appropriate for the question, and whether the timeline and budget are internally consistent. A common misunderstanding is that feasibility only matters for large, multi-year projects. In practice, even a small pilot study or a single animal experiment can be judged infeasible if the methods do not match the question or if the timeline is unrealistic. The same logic applies to student research proposals, fellowship applications, and institutional animal care protocols. The reviewer needs to see that you have thought through the practical steps from start to finish. ## The Core Components of a Feasible Specific Aim A feasible specific aim has four structural parts that must fit together. Each part answers a different question that a reviewer will have in mind. ### The Research Question and Hypothesis The aim must state a clear research question and a testable hypothesis. A vague question such as "investigate the role of nutrition in dairy calf health" does not tell the reviewer what you will measure, in which animals, or over what period. A feasible version might state that you will test whether a specific trace mineral supplement reduces the incidence of respiratory disease in preweaned calves over a 60 day period. The hypothesis should be specific enough that a negative result is informative. If every possible outcome supports your hypothesis, the experiment cannot fail and therefore cannot produce useful knowledge. Reviewers recognize this pattern and will question whether the aim is truly testable. ### Preliminary Data as the Foundation Preliminary data are the strongest evidence of feasibility. They show that you have already performed some version of the proposed work, that the methods work in your hands, and that the effect you expect to see is detectable with your measurement approach. Preliminary data can include pilot experiments, published work from your laboratory, or data from your own production records if you are working in an agricultural setting. For animal research, preliminary data might include a small pilot group showing that your sampling technique works, that your assay detects the analyte of interest, or that your animals tolerate the experimental procedure. If you do not have preliminary data, you can still argue feasibility by citing published methods from other groups, but this is weaker evidence. Reviewers will want to know why you believe the method will work in your specific setting. ### Methods That Match the Question Each aim must name the specific methods you will use to answer the question. The level of detail should be sufficient for a reviewer to judge whether the approach is sound. For an animal study, this includes the species, breed, age, sex, number of animals, housing conditions, diet, sampling schedule, and outcome measures. The methods must also match the level of analysis you propose. If you plan to measure gene expression, you need to describe the tissue collection, RNA extraction, and quantification method. If you plan to measure growth performance, you need to describe how and when animals will be weighed. A mismatch between the question and the method is a common reason that aims are judged infeasible. ### Timeline and Budget as Feasibility Evidence The timeline shows that you have thought about sequencing. Some steps must happen before others, and some steps take longer than a naive estimate would suggest. Animal breeding, acclimation, and regulatory approval all take time. The budget must cover the animals, feed, supplies, personnel, and any service fees such as histology or sequencing. Reviewers will check whether the timeline and budget are consistent with each other and with the methods. If you propose to enroll 200 animals but your budget only covers 50, the aim is not feasible. If you propose a 12 month study but the funding period is 12 months and you need 3 months for regulatory approval, the timeline is not feasible. ## At a Glance | Feasibility Component | What Reviewers Look For | Common Weakness | How to Strengthen | | --- | --- | --- | --- | | Research question | A specific, testable hypothesis with a defined population and outcome | Vague language that cannot be falsified | State the animal population, intervention, comparison, and outcome measure | | Preliminary data | Evidence that the methods work in your hands | No pilot data or reliance only on published work | Run a small pilot study or analyze existing production records | | Methods | A detailed protocol that matches the question | Methods too general or mismatched to the outcome | Describe species, numbers, sampling schedule, and assays | | Timeline and budget | A realistic sequence of steps with adequate resources | Timeline ignores regulatory approval or budget omits key costs | Build in buffer time and itemize every major cost | ## Building a Feasibility Checklist for Your Specific Aims A structured checklist can help you identify weaknesses before reviewers do. Work through each item and write a concrete answer. If you cannot answer an item, that gap is a feasibility risk. ### Step 1: Define the Population and Outcome Write down the exact animals you will study. Include species, breed, age range, sex, and any health or production status that matters. Then write down the primary outcome you will measure and how you will measure it. If the outcome is subjective, such as a lameness score, describe who will score it and how you will ensure consistency. ### Step 2: Estimate Animal Numbers Use the expected effect size, the variability you expect, and the statistical test you plan to use to estimate the number of animals needed. If you do not have the expertise to do this, consult a statistician before you submit. Reviewers will notice if the animal numbers are arbitrary or if the study is underpowered. ### Step 3: Map Each Aim to a Method For each aim, write the specific method in one sentence. Then list the supplies, equipment, and personnel needed to perform that method. If any item is not available at your institution, note how you will access it, such as through a core facility or a collaboration. ### Step 4: Build a Month by Month Timeline Start from the funding start date and work backward from the end of the funding period. Include time for regulatory approval, animal acquisition, acclimation, the experimental period, sample processing, data analysis, and manuscript preparation. Add a buffer of at least one month for unexpected delays. ### Step 5: Itemize the Budget List every major cost associated with each aim. Include animal purchase and per diem costs, feed, bedding, veterinary care, supplies, assays, personnel effort, and publication costs. Compare the total to the requested budget. If the total exceeds the budget, you must either reduce the scope or find additional support. ### Step 6: Identify Dependencies and Risks List every step that must succeed for the aim to be completed. For each step, ask what could go wrong and what you would do instead. A feasible proposal has a plan B for the most critical steps. For example, if your primary assay fails, do you have a backup assay or a collaborator who can run it? ## Records and Measurements That Support Feasibility The strongest feasibility arguments are built on records you already hold. If you are working with animals, your production records, health records, and breeding records are evidence that you understand the population and the setting. ### Production and Health Records If you propose to study dairy calves, your herd health records can show the baseline incidence of the disease you plan to study. If you propose to study growth performance, your past weight gain data can show the variability you expect. These records are preliminary data in the broadest sense because they demonstrate that you have access to the population and that you can measure the outcomes of interest. ### Pilot Study Logs A pilot study log that records dates, animal numbers, procedures, and problems is direct evidence of feasibility. It shows that you have actually performed the methods and that you know how long each step takes. Reviewers value this kind of practical evidence over a statement that the methods are standard. ### Equipment and Facility Documentation If your methods depend on specific equipment, document that the equipment exists and is functional. A letter from a core facility director confirming access and pricing is stronger than a statement that you will use the core. If you need animal housing with specific features, such as individual pens or controlled temperature, confirm that those facilities are available. ## Common Failure Patterns in Specific Aims Reviewers see the same feasibility problems repeatedly. Recognizing these patterns can help you avoid them. ### The Kitchen Sink Aim This aim tries to answer too many questions at once. It might propose to measure growth, immune function, behavior, and meat quality in the same animals with no clear primary outcome. The problem is that each additional outcome adds cost, time, and complexity. Reviewers will question whether you can do all of it well. The fix is to choose one primary outcome and treat the others as secondary. The primary outcome drives the sample size calculation and the timeline. Secondary outcomes can be exploratory, but they should not be the basis for the feasibility argument. ### The Vague Method Aim This aim names a technique but does not describe how it will be applied. For example, "we will use transcriptomics to study the immune response" does not tell the reviewer which tissue, which time points, or how many samples. The reviewer cannot judge whether the method is feasible because the method is not actually described. The fix is to write the method at the level of a protocol. Name the tissue, the time points, the number of samples, and the analysis pipeline. If you do not know these details, you are not ready to write the aim. ### The Unrealistic Timeline Aim This aim proposes more work than can fit in the funding period. A common version is a 12 month study that includes a 6 month breeding period, a 3 month experimental period, and 3 months of analysis, all within a 12 month grant. The math does not work. The fix is to build the timeline from the actual steps and to be honest about how long each step takes. If the work cannot fit, reduce the scope or propose a two phase study where the first phase is funded and the second phase is described as future work. ### The Budget Mismatch Aim This aim describes a study that costs more than the requested budget. The mismatch might be in animal numbers, assay costs, or personnel effort. Reviewers will notice and will question whether you have actually priced the work. The fix is to itemize the budget before you finalize the aims. If the cost exceeds the budget, you must change the aims or find additional funding. Do not assume that reviewers will not notice. ## Welfare and Safety Context in Feasibility Animal welfare and personnel safety are part of feasibility because they affect whether the study can be completed. An animal protocol that is not approved cannot start. A procedure that causes unrelieved pain or distress will be stopped by the institutional animal care and use committee. A study that puts personnel at risk will be halted by occupational health requirements. ### Regulatory Approval as a Timeline Item Institutional animal care and use committee approval is a required step before any animal work begins. The time to obtain approval varies, but it is never zero. Your timeline must include this period. If you are applying for funding, you can describe the approval as pending, but you should have a clear plan for obtaining it. The National Institutes of Health expects applicants to follow applicable laws, policies, and guidelines for the care and use of laboratory animals. Your feasibility argument should acknowledge that the proposed work will be conducted under an approved animal protocol and that you have the institutional support to obtain and maintain that approval. ### Pain and Distress Management If your proposed procedures cause more than momentary pain or distress, you must describe how you will manage it. This includes anesthesia, analgesia, and humane endpoints. A procedure that cannot be performed humanely is not feasible, regardless of the scientific value. ### Personnel Safety Some methods carry risks to personnel, such as exposure to zoonotic agents, anesthetic gases, or sharp instruments. Your feasibility argument should show that you have the training and equipment to perform the work safely. If a method requires specialized training, such as surgery or anesthesia, name the person who will perform it and describe their qualifications. ## Professional Escalation Criteria Some feasibility problems cannot be solved by rewriting the aims. Recognize when you need to escalate the issue to a supervisor, a mentor, a statistician, or an institutional official. ### When to Consult a Statistician Consult a statistician before you finalize your animal numbers. If you do not know the expected effect size, the variability, or the appropriate statistical test, you need expert input. A statistician can also help you design the study to avoid common pitfalls such as pseudoreplication or inadequate blinding. ### When to Consult a Veterinarian Consult a veterinarian if you are unsure whether the proposed procedures are humane or whether the animals can tolerate the experimental protocol. A veterinarian can advise on anesthesia, analgesia, surgical technique, and humane endpoints. Their input strengthens the feasibility argument because it shows that animal welfare has been considered. ### When to Consult an Institutional Official Consult your institutional animal care and use committee or research office if you are unsure about regulatory requirements. They can tell you what documentation is needed and how long approval will take. If you are applying for funding, they can also help you understand institutional policies on effort reporting, conflict of interest, and data management. ### When to Revise the Aims If the feasibility analysis shows that the work cannot be completed within the funding period or budget, revise the aims. This is not a failure. It is a normal part of proposal development. A focused aim that you can complete is stronger than an ambitious aim that you cannot. ## Data Management as a Feasibility Component Data management is part of feasibility because it affects whether you can analyze and share your results. The National Institutes of Health data management and sharing policy expects researchers to plan for how data will be managed and shared. Your feasibility argument should include a data management plan that covers storage, backup, documentation, and sharing. ### Data Storage and Backup Describe where data will be stored and how it will be backed up. For animal studies, this includes raw data files, laboratory notebooks, and any electronic records. A plan that relies on a single laptop with no backup is not feasible. ### Data Documentation Describe how data will be documented so that someone else can understand it. This includes variable names, units, and any coding schemes. Good documentation makes the data usable for analysis and for sharing. ### Data Sharing Describe how data will be shared after the study is complete. This might include depositing data in a public repository or making it available upon request. The data management plan should be consistent with the funding agency's expectations and with any institutional policies. ## Reporting Guidelines and Publication Ethics Feasibility also includes the plan for reporting your results. Reviewers want to see that you have thought about how the study will be reported and that you will follow accepted standards for transparent reporting. ### Selecting a Reporting Guideline The EQUATOR Network maintains a collection of reporting guidelines for different study types. For animal studies, the relevant guideline depends on the study design. Using a reporting guideline from the planning stage helps ensure that your methods and results are described completely and transparently. This is part of feasibility because it affects whether your results can be interpreted and used by others. ### Authorship and Publication Ethics The Committee on Publication Ethics core practices describe expectations for authorship, peer review, data, and conflicts of interest. Your feasibility plan should include a plan for authorship that is agreed upon before the work starts. Disputes about authorship can delay publication and can damage collaborations. ### Researcher Identity and Records Maintaining an accurate researcher record is part of professional practice. An ORCID identifier provides a persistent link between you and your research outputs. Keeping this record current is a small but practical step that supports the long term impact of your work. ## Practical Steps to Test Feasibility Before Submission You can test the feasibility of your aims before you submit the application. These steps take time, but they are less costly than a rejected application. ### Run a Mini Pilot If you have access to animals and the necessary approvals, run a small pilot study. The pilot does not need to be large enough for statistical power. It needs to show that the methods work, that the animals tolerate the procedures, and that you can collect the samples or measurements you need. ### Write the Protocol in Full Write the full protocol for one aim as if you were going to start tomorrow. Include the exact number of animals, the exact procedures, the exact timeline, and the exact supplies. If you cannot write this protocol, you do not yet have a feasible aim. ### Price the Work Call the suppliers and the core facilities. Get actual prices for the animals, the feed, the assays, and the services. Compare the total to your budget. If the total is higher than the budget, you have a feasibility problem that needs to be solved before submission. ### Walk Through the Timeline Write the timeline month by month and check it against the funding period. Include regulatory approval, animal acquisition, acclimation, the experimental period, sample processing, data analysis, and manuscript preparation. If the timeline does not fit, revise the scope. ## Limitations of Feasibility Arguments Feasibility arguments have limits. A well written feasibility section does not guarantee that the study will succeed. Unexpected problems will arise, and some of them cannot be anticipated. ### The Limits of Preliminary Data Preliminary data show that the methods worked in a small sample or in your hands under specific conditions. They do not guarantee that the methods will work at a larger scale or in a different setting. Scaling up can introduce new problems, such as equipment capacity, personnel availability, or animal supply. ### The Limits of Published Methods Published methods from other groups show that a technique is possible, but they do not show that you can perform it. Differences in equipment, personnel, and animal populations can change the outcome. If you rely on published methods, you should have a plan for verifying that the method works in your setting. ### The Limits of a Timeline A timeline is a plan, not a guarantee. Delays in animal delivery, equipment failure, and personnel turnover can all push the timeline back. A buffer helps, but it cannot absorb every possible delay. Reviewers know this, which is why they look for a realistic timeline instead of an optimistic one. ## Frequently Asked Questions ### How do I know if my specific aim is too ambitious? Compare the scope of the aim to the funding period and budget. If the timeline does not fit or the budget does not cover the work, the aim is too ambitious. A useful test is to write the full protocol and price every step. If you cannot complete the protocol within the funding period, reduce the scope. ### What counts as preliminary data for a feasibility argument? Preliminary data are any evidence that the methods work in your hands. This includes pilot experiments, published work from your group, and analysis of existing records such as production or health data. The strongest preliminary data come from a pilot study that uses the same methods and population you propose. ### How many animals do I need for a feasible study? The number depends on the expected effect size, the variability, and the statistical test. There is no universal number. Consult a statistician to estimate the sample size before you finalize the aims. A study with too few animals is not feasible because it cannot answer the question. ### What should I do if I do not have preliminary data? You can still argue feasibility by citing published methods and by describing your access to the population and facilities. However, this is weaker evidence. Consider running a small pilot study before you submit, or propose a smaller first aim that generates the preliminary data for a larger future study. ### How do I write a timeline that reviewers will believe? Build the timeline from the actual steps, starting with regulatory approval and ending with manuscript submission. Include buffer time for unexpected delays. Reviewers will believe a timeline that shows you have thought about sequencing and realistic durations. ### What is the most common reason specific aims are judged infeasible? The most common reason is a mismatch between the scope of the aims and the available time or budget. This often happens when the applicant has not priced the work or has not built the timeline from the actual steps. A detailed protocol and an itemized budget prevent this problem. ### Should I include a backup plan in my feasibility section? Yes. A backup plan for the most critical steps shows that you have thought about risks. For example, if your primary assay might fail, name a backup assay or a collaborator who can run it. This strengthens the feasibility argument because it shows that the study can still be completed if a step goes wrong. ### How does data management affect feasibility? Data management affects whether you can analyze and share your results. A plan that covers storage, backup, documentation, and sharing is part of a feasible study. Without a data management plan, you risk losing data or being unable to share it as required by the funding agency. ## Using the Evidence | Source | Best use in this topic | Important limitation | |---|---|---| | [Research Methods Resources](https://www.ncbi.nlm.nih.gov/books) | official guidance | Check the linked page for current local requirements | | [EQUATOR Network](https://www.equator-network.org/) | official guidance | Check the linked page for current local requirements | | [Core Practices](https://publicationethics.org/core-practices) | official guidance | Check the linked page for current local requirements | ## Related Articles - [Feasibility of a Research Study: How to Assess Before You Start](/blog/guides/feasibility-of-a-research-study-how-to-assess-before-you-start) - [Sample Size Symbol and Notation: What You Need to Know](/blog/guides/sample-size-symbol-and-notation-what-you-need-to-know) - [Cosmetics Regulatory Affairs Jobs: What You Need to Know](/blog/careers/cosmetics-regulatory-affairs-jobs-what-you-need-to-know) - [Bioinformatics Careers: What You Need to Know Before You Start](/blog/careers/bioinformatics-careers-what-you-need-to-know-before-you-start) - [Bloodborne Pathogens Training for Healthcare Workers: What You Need to Know](/blog/careers/bloodborne-pathogens-training-for-healthcare-workers-what-you-need-to-know) ## References and Further Reading - [Research Methods Resources](https://www.ncbi.nlm.nih.gov/books). National Library of Medicine. - [EQUATOR Network](https://www.equator-network.org/). EQUATOR Network. - [Core Practices](https://publicationethics.org/core-practices). Committee on Publication Ethics. - [NIH Grants and Funding](https://grants.nih.gov/). National Institutes of Health. - [ORCID for Researchers](https://info.orcid.org/researchers). ORCID. - [Data Management and Sharing Policy](https://sharing.nih.gov/data-management-and-sharing-policy). National Institutes of Health. - [NCBI Literature Resources](https://www.ncbi.nlm.nih.gov/). National Center for Biotechnology Information. - [PubMed](https://pubmed.ncbi.nlm.nih.gov/). National Library of Medicine. > This article is educational and does not replace institutional policy, professional advice, or applicable safety and regulatory requirements.