Part 2 · Module 6
Developing Research Hypotheses
Learn when and how to state research and statistical hypotheses that are testable, pre-specified and aligned with the study question and design.
Learning outcomes
After completing this lecture, you should be able to:
- explain the role of a hypothesis in focused quantitative research
- distinguish research, null and alternative hypotheses
- write directional and non-directional hypotheses using clearly defined populations and variables
- recognise situations in which a formal hypothesis is unnecessary or inappropriate
- appraise whether a hypothesis is testable, pre-specified and supported by the proposed design and analysis
Overview
A research hypothesis is a reasoned, testable expectation about a relationship, difference or effect. It is developed from the research problem, theory and prior evidence before the results are examined. In focused quantitative research, a hypothesis can sharpen the primary comparison, clarify the expected direction and connect the research question to statistical analysis.
Not every study requires a formal hypothesis. A prevalence survey may primarily estimate a proportion. A qualitative study may explore meanings without predicting them. An early feasibility study may focus on recruitment, acceptability and procedural uncertainties rather than testing effectiveness. The use of a hypothesis should follow the purpose of the study rather than academic convention [1–3].
This lecture distinguishes a substantive research hypothesis from the null and alternative hypotheses used in statistical testing. Detailed mechanics of hypothesis tests and p-values belong in the Biostatistics Lecture-Note Series.
Why this matters
Poorly formed hypotheses can misdirect design and interpretation. A hypothesis may be too vague to test, may include variables that are never measured, may predict causation from an unsuitable design or may be written after the results are known. Post hoc hypotheses can be useful for generating future research, but they should not be presented as pre-specified confirmation.
A strong hypothesis makes the expected contrast explicit without guaranteeing the result. It helps the team choose the primary outcome, comparator, follow-up time and analysis. It also reveals when the study is exploratory and should be described as such.
Research and statistical hypotheses
Research hypothesis
The research hypothesis is expressed in substantive clinical or methodological terms.
Women offered a scheduled postpartum glucose-test appointment plus an automated reminder will have higher testing completion within 12 weeks than women receiving the current referral pathway.
It identifies the population, strategies, outcome, time and expected direction.
Null hypothesis
The statistical null hypothesis commonly represents no difference or no association in the target parameter.
The probability of testing completion within 12 weeks is the same under the scheduled appointment plus reminder pathway and the current referral pathway.
Alternative hypothesis
The statistical alternative represents a difference or a specified direction.
The probability of completion differs between the pathways.
For a one-sided alternative, the direction would be specified. A one-sided test requires strong prospective justification because effects in the unexpected direction may still matter clinically [4].
When a hypothesis is useful
A formal hypothesis is often useful when the study:
- compares an intervention with a control or alternative;
- examines a pre-specified exposure–outcome association;
- tests a theory-derived relationship;
- evaluates a planned interaction or mediation pathway; or
- seeks confirmatory evidence about a defined primary outcome.
The hypothesis is most useful when it influences design or analysis. Adding a generic statement such as “there is a significant relationship between all variables” contributes little.
When a hypothesis may not be needed
Descriptive estimation
A study estimating prevalence, mean outcome, service coverage or diagnostic sensitivity can be driven by an estimation objective. A null hypothesis of zero prevalence or no sensitivity is usually not scientifically meaningful.
Exploratory qualitative inquiry
Qualitative research may begin with sensitising concepts or theoretical propositions, but a rigid prediction can constrain discovery. The research question and methodological orientation often provide better guidance.
Evidence mapping
A scoping review that maps available research does not need to hypothesise how many studies will be found.
Feasibility research
A pilot or feasibility study should address uncertainties about whether and how a future definitive study can be done. It may use progression criteria rather than a preliminary test of clinical effectiveness [5].
Model development
Prediction-model studies require pre-specified aims and performance criteria, but a simple null hypothesis is not the central organising device. Model performance, validation and calibration are more relevant.
Directional and non-directional hypotheses
A directional hypothesis predicts whether an outcome is higher, lower, better or worse.
Testing completion will be higher in the intervention group.
A non-directional hypothesis predicts a difference without specifying direction.
Testing completion will differ between groups.
A directional research hypothesis does not automatically justify a one-sided statistical test. Unexpected harm or reversal may be clinically important, and two-sided inference is generally appropriate unless the protocol provides a compelling rationale for a one-sided procedure.
Examples of hypothesis forms
illustrative| Study purpose | Research hypothesis | Comment |
|---|---|---|
| Intervention | A scheduled appointment plus reminder increases 12-week completion compared with usual referral | Directional and comparative |
| Association | Longer travel distance is associated with lower completion | Does not by itself claim causality |
| Interaction | The effect of the reminder differs according to previous gestational-diabetes history | Requires adequate design and power |
| Descriptive prevalence | Postpartum completion is 30% | Usually better framed as an estimation target or planning assumption than a confirmatory hypothesis |
| Qualitative experience | Women will report inconvenience | Risks constraining inquiry; use an open question instead |
Characteristics of a testable hypothesis
It is aligned with one question and objective
The hypothesis should not introduce a new population, exposure or outcome. If the primary objective estimates completion, a hypothesis about quality of life belongs elsewhere unless it is a genuine secondary aim.
Variables and timing are operationally definable
“Better follow-up” must become a specified outcome, such as a documented test within 12 weeks. “High knowledge” requires an instrument and threshold or a planned continuous scale.
The proposed design can address the claim
A causal hypothesis requires a design and assumptions capable of estimating a causal effect. A cross-sectional association cannot establish that exposure preceded outcome.
It is plausible and evidence-informed
A hypothesis should arise from theory, prior evidence, biological reasoning or a documented mechanism. It should not be reverse-engineered solely to obtain a statistically significant result.
It is stated prospectively
The hypothesis and primary outcome should be recorded before unblinded outcome analysis. Changes may be necessary, but they should be dated, justified and reported transparently.
Simple, complex and multiple hypotheses
A simple hypothesis concerns one main exposure or intervention and one main outcome. A complex hypothesis includes multiple predictors, outcomes, mediators or interactions. Complexity can be scientifically appropriate, but each additional element increases design and sample-size demands.
Avoid using one broad hypothesis to cover every variable. A statement that sociodemographic, behavioural, clinical and service factors “significantly influence” an outcome is neither prioritised nor mechanistically clear. Pre-specify the primary relationship and classify other analyses appropriately.
Hypotheses and causal language
“Associated with” states a statistical relationship. “Affects”, “causes”, “influences” and “leads to” imply a causal contrast. The wording should match the question and design.
For example:
- Associational: Women without a scheduled appointment have lower observed completion.
- Causal: Offering a scheduled appointment reduces non-completion.
The causal statement concerns what would happen under alternative strategies. It requires attention to assignment, confounding, adherence, missing outcome data and co-interventions.
Superiority, non-inferiority and equivalence hypothesesMMed extension
A superiority study asks whether one strategy performs better than another. A non-inferiority study asks whether a new strategy is not worse than the comparator by more than a pre-specified clinically acceptable margin. An equivalence study asks whether effects fall within both sides of a pre-specified equivalence margin. These are distinct questions with different hypotheses, sample-size requirements and interpretation; failure to show superiority does not establish non-inferiority or equivalence.
Worked example
Aligning the postpartum intervention hypothesis
Question: Among women with gestational diabetes, does a scheduled appointment plus automated reminder, compared with usual referral, increase documented postpartum glucose testing within 12 weeks?
Primary objective: To compare documented testing completion within 12 weeks between women allocated to the enhanced pathway and usual referral.
Research hypothesis: Completion within 12 weeks will be higher under the enhanced pathway.
Null hypothesis: The completion probabilities are equal between pathways.
Alternative hypothesis: The completion probabilities differ between pathways.
Primary outcome: Documented completion of the specified glucose test within 12 weeks, obtained through linked laboratory records.
Interpretation safeguard: The study will report the risk difference or risk ratio with a 95% confidence interval, not only a p-value. The estimate will be interpreted in relation to clinical importance, adherence, missing records and the validity of the comparison.
Appraising a hypothesis
Check the hypothesis
- Does it arise from the stated problem, question and evidence?
- Are population, exposure or intervention, comparator, outcome and timing identifiable?
- Is the predicted relationship or difference testable with the planned data?
- Does the wording avoid causal claims unsupported by the design?
- Was the hypothesis specified before outcome analysis?
- Is direction justified, and is a two-sided analysis still appropriate?
- Is one primary hypothesis prioritised rather than a vague set covering all variables?
- Will estimates and confidence intervals be reported alongside any hypothesis test?
Suggested protocol wording
Common mistakes
Knowledge check
Does a prevalence study need a null hypothesis that prevalence equals zero?
Answer and reasoning
Usually not. The scientifically useful task is to estimate prevalence with uncertainty. A zero-prevalence null may be implausible and does not organise the design as effectively as a clear estimation objective.
What is the difference between a research hypothesis and a null hypothesis?
Answer and reasoning
The research hypothesis states the substantive expected relationship or difference. The null hypothesis is a statistical statement—often no difference or association—used within a testing framework.
Why should a hypothesis be written before analysing outcomes?
Answer and reasoning
Prospective specification distinguishes confirmatory testing from patterns discovered in the data. Post hoc findings can generate hypotheses, but presenting them as pre-specified overstates the evidence.
A trial does not find a statistically significant superiority result. Can the investigators conclude equivalence?
Answer and reasoning
No. Equivalence requires a prospectively specified equivalence margin and an appropriate design and analysis. Failure to demonstrate superiority may reflect limited precision and does not establish similarity.
Key take-home messages
Key take-home messages
- A hypothesis is a prospective, testable expectation derived from the question, evidence and theory.
- Research, null and alternative hypotheses serve different functions.
- Formal hypotheses are useful for focused comparative and associational questions but are not compulsory for every study.
- Define the population, variables, comparator, outcome and timing clearly enough to test the claim.
- Match causal wording to the design and assumptions.
- Report effect estimates and confidence intervals; a hypothesis test alone does not establish clinical importance or absence of effect.
References
- Farrugia P, Petrisor BA, Farrokhyar F, Bhandari M. Research questions, hypotheses and objectives. Can J Surg. 2010;53(4):278–281.
- Browner WS, Newman TB, Cummings SR, Grady DG, Huang AJ, Kanaya AM, et al. Designing clinical research. 5th ed. Philadelphia: Wolters Kluwer; 2022.
- World Health Organization Regional Office for the Eastern Mediterranean. A practical guide for health researchers. Cairo: World Health Organization; 2004. https://iris.who.int/handle/10665/119703
- Bland JM, Altman DG. One and two sided tests of significance. BMJ. 1994;309(6949):248. https://doi.org/10.1136/bmj.309.6949.248
- Eldridge SM, Chan CL, Campbell MJ, Bond CM, Hopewell S, Thabane L, et al. CONSORT 2010 statement: extension to randomised pilot and feasibility trials. BMJ. 2016;355:i5239. https://doi.org/10.1136/bmj.i5239
These materials support learning and research planning. They require adaptation to the study context and do not replace methodological, statistical, ethical, or regulatory consultation.