The research proposal: question, hypotheses, aims and timeline

A research proposal is a plan that persuades a reader that a project is worth doing and can be done. In a career in research, proposals are how scientists win grants, gain access to field sites and obtain approvals. In an honours project, the proposal is usually the first formal deliverable, often marked, and it is the document your supervisor, second reader and approval committees will use to judge your plans. Writing it forces you to make decisions you might otherwise postpone, and most of it, revised, becomes the introduction and methods of your thesis.

Priya's department required a proposal of no more than 2,500 words by the end of March, with a ten-minute oral presentation to her supervisor and second reader. This lesson walks through her proposal section by section and shows how she handled the parts students often find hardest: aims, the budget, the timeline and the plan for things going wrong.

The structure of a proposal

Proposal formats vary among departments and funding agencies, but most contain the same elements in roughly the same order.

The sections answer four questions a reviewer always asks: why should this be done, how will it be done, can it be done in the time and budget, and what difference will the results make? A good proposal makes the answers easy to find. Headings that match the department's required sections, short paragraphs and a clear figure of the study design all help.

Title and summary

The title should state the question or system precisely. Priya's working title was Does cattle exclusion improve chorus frog breeding in prairie pothole wetlands? A question as a title is acceptable in a proposal; theses and papers more often use a statement. The summary, usually 150 to 250 words, gives the problem, the question, the approach and the expected significance in plain language. Write it last, when you know what the proposal says.

Background and rationale

The background is a condensed literature review, usually a third to a half of the proposal. It should establish three things: the problem matters, something important is unknown, and your project will address that unknown. Priya's background had three paragraphs on the importance of prairie pothole wetlands and their amphibians, the ways cattle alter wetland margins, and the mixed evidence on livestock effects on amphibians, ending with the gap: no paired test in prairie potholes and little measurement of mechanisms. She cited 24 sources, chosen from the 42 in her full review.

Aims and objectives

Aims are broad statements of what the project will achieve; objectives are the specific, measurable tasks that achieve them. Students often confuse the two or list too many. Two or three aims, each with two or three objectives, are enough for an honours project.

Each objective can be checked off: either Priya measured turbidity at 24 wetlands on four visits or she did not. Objectives written this way are also the backbone of the methods section and the timeline.

Methods and analysis plan

The methods section should give enough detail that a reader could judge whether the design answers the question: the study sites, the design and its replication, each measurement with its method and timing, and how the data will be analysed. Lesson 5 covers design in detail. In a proposal, an analysis plan matters as much as the field or lab methods, because the most common reason projects disappoint is that the data collected cannot answer the question. Priya's plan stated that the main comparison would be the mean within-pair difference in log tadpole density, fenced minus grazed, estimated with a 95 in 100 confidence interval and tested with a paired permutation test, and that the relationship between turbidity and tadpole density would be examined with a regression across all 24 wetlands.

Writing the analysis plan before collecting data has a further benefit. Researchers who decide on their analysis after seeing the data are tempted, often unconsciously, to choose the version that gives the most striking result. Stating the main analysis in advance, and labelling any additional analyses as exploratory, protects against this. Some researchers go further and publicly preregister their hypotheses and analysis plan, for example on the Open Science Framework, before collecting data. Priya did not formally preregister, but her dated proposal served the same purpose, and her thesis distinguished planned from exploratory analyses.

The timeline

A timeline shows when each objective will be done, how tasks depend on each other, and where the deadlines fall. A Gantt chart, with tasks as rows and time as columns, is the usual format. The critical path is the chain of dependent tasks that determines the earliest possible finish; any delay on it delays the whole project. For Priya, the critical path ran through approvals, fieldwork, lab analysis, data analysis and writing. Approvals had to be granted before the frogs began calling in late April, and lab analysis could not begin until the samples were collected.

# A simple critical-path check: each task has a duration (weeks) and the tasks it depends on.
tasks = {
    "Proposal":      (6, []),
    "Approvals":     (8, ["Proposal"]),
    "Field season":  (12, ["Approvals"]),
    "eDNA lab work": (10, ["Field season"]),
    "Recorder data": (6, ["Field season"]),
    "Analysis":      (8, ["eDNA lab work", "Recorder data"]),
    "Writing":       (14, ["Analysis"]),
}
finish = {}
def end(t):
    if t not in finish:
        dur, deps = tasks[t]
        finish[t] = dur + max((end(d) for d in deps), default=0)
    return finish[t]
for t in tasks:
    print(f"{t:14s} finishes at week {end(t):3d}")
print("Critical path length:", max(finish.values()), "weeks")

Proposal       finishes at week   6
Approvals      finishes at week  14
Field season   finishes at week  26
eDNA lab work  finishes at week  36
Recorder data  finishes at week  32
Analysis       finishes at week  44
Writing        finishes at week  58
Critical path length: 58 weeks

The script shows that the project, done strictly in sequence, needs 58 weeks. That is longer than an academic year, which is why real timelines overlap tasks. Priya began her data analysis on the recorder files in October while the eDNA lab work was still running, and she drafted her introduction and methods during the lab work, so that by the time the analysis was finished, only the results and discussion remained to be written. Planning these overlaps is one of the main reasons to make a timeline.

The budget

An honours budget is usually small, but it must be realistic and justified. List each item, its unit cost, the quantity and the total, and explain any item a reviewer might question. Remember costs that are easy to forget: shipping, safety equipment, mileage, replacement of broken equipment and the cost of repeating a failed analysis.

# Priya's proposal budget (Canadian or US dollars; amounts are illustrative).
items = [
    ("eDNA filters and sampling kits", 80, 6.50),     # 72 field samples plus 8 field blanks
    ("DNA extraction kits (per sample)", 80, 9.00),
    ("qPCR reactions incl. controls", 480, 2.10),
    ("Dip-net and field supplies", 1, 140.00),
    ("Turbidity meter calibration standards", 1, 95.00),
    ("Mileage to field station (km)", 2400, 0.30),
]
total = 0
for name, qty, unit in items:
    cost = qty * unit
    total += cost
    print(f"{name:38s} {qty:5d} x {unit:6.2f} = {cost:8.2f}")
print(f"{'Total':38s} {'':16s} {total:8.2f}")
print(f"With a 10 in 100 contingency:            {total * 1.10:8.2f}")

eDNA filters and sampling kits            80 x   6.50 =   520.00
DNA extraction kits (per sample)          80 x   9.00 =   720.00
qPCR reactions incl. controls            480 x   2.10 =  1008.00
Dip-net and field supplies                 1 x 140.00 =   140.00
Turbidity meter calibration standards      1 x  95.00 =    95.00
Mileage to field station (km)           2400 x   0.30 =   720.00
Total                                                    3203.00
With a 10 in 100 contingency:             3523.30

The total of about 3,200 dollars was within Dr. Okafor's limit. The number of qPCR reactions, 480, came from 72 field samples and 8 field blanks, each run in triplicate, plus extraction blanks, standards and no-template controls on each plate. Explaining that arithmetic in the proposal showed the reviewers that Priya understood her methods. The contingency line, an extra 10 in 100 for unexpected costs, proved wise: inhibition in the turbid samples meant that about 100 extra reactions were needed to rerun diluted extracts.

Risks and contingencies

Every project can go wrong, and a proposal that admits this is more convincing than one that does not. A risk table lists what could go wrong, how likely it is, how serious it would be and what you will do about it.

Two kinds of fallback are especially valuable. First, design the project so that each aim produces results independently, so that the failure of one does not ruin the others. Priya's three aims shared sites but not methods: if the eDNA failed, the calling and tadpole data could still answer aims 1 and 2. Second, plan what you will do with the data if the main comparison shows nothing. A well-estimated small or zero effect, with a clear confidence interval, is a publishable result; Priya's proposal said so explicitly.

Expected outcomes and significance

The final section explains what the results could mean. Avoid promising a particular result. Instead, describe the possible outcomes and the value of each. Priya wrote that if fenced wetlands had more tadpoles and clearer water, the results would support fencing programs as amphibian conservation measures; if turbidity differed but tadpole density did not, the results would suggest that other factors limit chorus frogs; and if eDNA detections matched calling records, the method could be used to survey breeding more cheaply. She also noted who would use the results: the ranch, the provincial or state agency that supports fencing, and conservation organizations.

Presenting and revising the proposal

Priya's ten-minute presentation used six slides: the problem, the gap, the question and predictions, a map of the design, the timeline and the risks. Her second reader, Dr. Szabo, asked one hard question: with only 12 pairs, could she detect an effect of the size she expected? She had not calculated this. He suggested a power analysis, which lesson 5 describes, and her revised proposal included one. Feedback of this kind is the purpose of a proposal review, and the best response is to thank the reviewer and revise, not to defend the original plan.