Key point: A significant result may still be too small to matter.
Understand why a low p value is not enough and how effect sizes and confidence intervals improve quantitative research reporting.
Two different questions
Statistical significance addresses how compatible the observed data are with a stated null model under specified assumptions. Effect size addresses the magnitude of a difference or relationship. These are not interchangeable questions.
Why sample size changes the picture
A very large sample may produce a small p value for an effect that has little practical importance. A smaller study may estimate a meaningful effect imprecisely. The estimate and its confidence interval provide essential context.
What to report
Report descriptive statistics, the test result, exact p value where appropriate, effect size, confidence interval and a contextual interpretation. Use the measure of effect size that fits the design and analysis.
Interpretation before conclusion
Ask whether the magnitude would change a real decision, improve practice or advance theory. Statistical evidence should support scientific reasoning rather than replace it.
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About the author
Kazeem Afen
Kazeem Afen is an academic research consultant at Speedrise Research. He supports undergraduate, master’s and doctoral researchers with research development, academic editing, quantitative and qualitative analysis, presentations and responsible manuscript revision.