SCIF1000 Chap.2 Research Questions, Aims and Hypotheses
Research Questions, Aims and Hypotheses
A topic is not yet a scientific question. This chapter distinguishes the broad aim from the focused question and the specific hypothesis. It shows how to identify independent and dependent variables, operationalise vague concepts, describe a comparison and draw the predicted evidence pattern before results are known.
The recurring test is whether the statement is observable, testable and falsifiable under defined conditions.
An aim preserves purpose. It can explain why attention, biodiversity or public trust matters without pretending that the complete problem will be resolved in one activity. The question performs the next narrowing step by defining the system, comparison, outcome and condition.
If one of those elements remains hidden, different team members may unknowingly design different studies.
Operational definitions convert concepts into procedures. They should be precise enough for another person to repeat while remaining honest about what they represent. A timed recall score can indicate one aspect of learning, but it is not identical to durable academic achievement.
Naming that gap prevents a convenient measure from expanding into a larger claim during discussion.
The hypothesis records the expected pattern before results appear. A directional hypothesis should identify which condition is expected to have the larger, smaller or differently shaped response. Drawing the graph forces choices about axes, units and groups.
It also creates a visible failure condition: an observed pattern that does not support the prediction under the specified design.
Question quality includes relevance as well as testability. Explain which decision the result could inform and whose outcome is represented. Then identify the strongest alternative explanation.
A well-designed answer does not merely state a preferred mechanism; it shows how the comparison reduces rivals and which rival remains. The final conclusion must use a verb no stronger than the study design permits.
What this chapter covers
- 01
Research aims and focused questions
- 02
Observable, testable and falsifiable hypotheses
- 03
Independent and dependent variables
- 04
Operational definitions and predicted graphs
- 05
Causal language and design boundaries
Worked example · free
Repair a vague attention claim
- +1Define the participants, exposure conditions and one measured outcome.
- +1Write a directional prediction that could fail and name the independent and dependent variables.
- +1State what the design would not establish and name one alternative explanation.
Key terms
- Research aim
- The broad purpose or goal that gives an investigation direction without specifying the complete test.
- Hypothesis
- A specific proposition predicting an observable relationship or difference that evidence could fail to support.
- Operational definition
- The exact procedure or measure used to represent a concept in a particular investigation.
- Falsifiability
- The property of a claim that allows a possible observation to count against its prediction.
Research Questions, Aims and Hypotheses FAQ
What is the difference between an aim and a hypothesis?
An aim states the broad purpose of a project. A hypothesis narrows that purpose into a specific predicted pattern involving observable variables under defined conditions.
How do I identify the variables?
The independent variable organises the conditions, exposure or predictor. The dependent variable is the recorded response. In an observational design, those labels do not by themselves establish causation.
Why should I sketch the predicted graph first?
The sketch forces the group to agree on axes, units, groups and direction before results appear. Disagreement about the graph often reveals that the hypothesis remains ambiguous.
When should a hypothesis include an exact effect size?
Use a numerical prediction when prior evidence, a mechanism or a meaningful decision threshold supports that precision. An exact value should not be invented merely to make the sentence appear more scientific. If the evidence supports only a direction, write a directional hypothesis and separately identify the effect size that would matter in practice.
If even the direction is uncertain, frame competing predictions or an estimable question. In every case, define the measure, population, comparison and time point so the result can genuinely support or challenge the stated prediction.
How can I test whether my research question is unambiguous?
Give the question to a peer without explaining it and ask them to identify the population, variables, comparison, unit of analysis and time frame. Then ask them to sketch the expected graph and describe what observation would count against the hypothesis. Differences between their reconstruction and yours reveal hidden choices in the wording. Repair those choices before selecting a method.
This test is especially useful for terms such as improvement, exposure, effectiveness and wellbeing, which sound specific but can represent several measures. Clear wording should constrain interpretation without pretending the study can answer more than its design permits.
Assessment move
Practise transforming broad claims into a four-part question: system, comparison, outcome and condition. For each hypothesis, draw the expected pattern and write one observation that would count against it. Then check whether the conclusion verb matches the design. Use causal wording only when the comparison supports it.
Finish every practice response with a boundary sentence explaining what the study tests and what larger claim it does not establish.
