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Data analysis Student writing guide

Statistical Significance, P-Values and Student Project Data Analysis

Data analysis begins with the research question and the way evidence was collected. A statistically significant result is not automatically large, important, causal or reproducible. The American Statistical Association’s statement on p-values sets out principles for interpreting them; the analysis method itself must still fit the design, variables and assumptions of your study.

A practical guide from FINAL YEAR NG Check your department's current instructions before you submit.

Map questions to variables and evidence

  1. Write each research question and objective in specific terms.
  2. Identify the outcome, predictors or qualitative evidence relevant to that question.
  3. Check how each variable was measured, coded and collected.
  4. Choose descriptive summaries or an inferential approach that fits the design and measurement scale.
  5. Check assumptions, missingness, exclusions and sample limitations before interpreting outputs.
  6. Plan a readable result table or figure with labels, units and an explanation in words.

A questionnaire does not determine the analysis by itself. Consider the question, sampling, comparison, measurement and dependence between observations. Ask a supervisor or qualified methods instructor to review an unfamiliar test before relying on it.

Interpret p-values carefully

A p-value describes how incompatible observed data are with a specified statistical model under its assumptions. It is not the probability that the research hypothesis is true, nor the probability that chance alone produced the data. The ASA also cautions that a p-value does not measure effect size or practical importance and that decisions should not rest only on whether a threshold was crossed.

  • Report the estimate or group difference, not only a p-value.
  • Where appropriate, report an interval or another uncertainty measure and explain what it represents.
  • State the sample and analysis decisions relevant to interpreting the result.
  • Describe whether the result is compatible with the question and design; do not claim causation from an observational association.
  • Report null or unexpected results honestly, including relevant limitations.

Write a transparent result

Hypothetical reporting pattern

In this synthetic teaching dataset, describe the sample, the measured variables, the comparison or association estimate, its uncertainty and the limitations. Label it synthetic every time it appears. Do not turn an example output into a finding about real students or a claim about a population.

Sources and further reading

These university writing resources inform the general advice here. They do not replace your department's handbook or supervisor's guidance.

  1. American Statistical Association: Statement on Statistical Significance and P-Values
  2. OpenLearn: Getting started with statistics
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