How to Interpret a P-Value in Research

How to Interpret a P-Value in Research

A plain-English explanation of p-values, statistical significance, effect sizes and why p-values should not stand alone.

Why this matters

Statistical decisions should follow the research question, study design, variable types and assumptions. A method that works for one study may be inappropriate for another.

A practical framework

1. Start with the research question

Decide whether the goal is description, comparison, association, prediction, explanation or time-to-event analysis.

2. Understand your variables

Identify outcome and predictor variables, measurement scales, groups, repeated observations and missing-data patterns.

3. Check the study design and assumptions

Independence, distributional assumptions, sample size, linearity, variance structure and other diagnostics can affect the appropriate method.

4. Report more than a p-value

Where appropriate, explain effect sizes, confidence intervals, model estimates, uncertainty and practical meaning alongside statistical significance.

When to get expert help

If you are unsure which method to use, have a complex dataset or need help interpreting existing output, a short statistical consultation can prevent avoidable analysis problems later.