How to justify sample size in a quantitative research proposal
Build a sample-size justification around your research goal, assumptions, and access limits. Includes a worked power example and a proposal checklist.
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Topic guidance
Data, Statistics & Interpretation
Understand measures, uncertainty and the limits of what results allow you to claim.
Find a workable starting point
Understand the evidence
Check what was measured, who contributed data and which observations support each result.
Separate findings from interpretation
Report the result accurately before explaining its meaning. Do not turn an association into a causal conclusion.
Make uncertainty visible
Discuss assumptions, precision and limitations alongside the result. These guides do not select a statistical method for an unseen dataset.
Your next step
Identify the variables, denominators, study design and uncertainty before choosing an interpretation.
Editing and revision apply across all six topics. Choose a resource by the task it helps you complete.
Suggested starting points
Results versus discussion: how to separate findings from interpretation
Decide what belongs in results and discussion, reduce repetition, and keep interpretations within the evidence. Includes a worked revision example.
Explore this topic
Write alt text that preserves a research figure's message
Describe a research figure’s essential information, separate short alt text from longer detail and check descriptions against the final figure version.
Choose a chart that answers the comparison question
Match the chart to the pattern you need to show, check the scale and baseline, and keep the display focused on one clear message.
Write table and figure captions that readers can understand
Make tables and figures intelligible with a display-to-caption map, fictional before-and-after examples, precise notes and a blind-read check.
Describe an association without overclaiming causation
Use a causal-claim audit to test the question, timing, design and rival explanations before turning an observed association into an effect claim.
Report missing observations and changing denominators
Trace who contributes to each result, label every percentage denominator, and report why analysis sample sizes change without hiding missing observations.
Build a descriptive-statistics table around your variables
Match each variable to a useful descriptive summary, show valid and missing denominators, and build a clear table from a checked fictional dataset.
Distinguish statistical significance from practical importance
Judge whether an estimated effect matters by reading its magnitude and uncertainty against a justified practical threshold—not a p-value alone.
Explain a confidence interval without overstating certainty
Turn a confidence interval into a bounded interpretation by naming the estimate, limits, procedure and assumptions—without converting confidence into certainty.
Results versus discussion: how to separate findings from interpretation
Decide what belongs in results and discussion, reduce repetition, and keep interpretations within the evidence. Includes a worked revision example.
How to justify sample size in a quantitative research proposal
Build a sample-size justification around your research goal, assumptions, and access limits. Includes a worked power example and a proposal checklist.