Short answer
Statistical significance and practical significance answer different questions: what a statistical test indicates, and whether the finding matters in its setting. A statistically significant result does not establish a large or important effect; interpreting it also requires effect magnitude, uncertainty, and context. 1 2
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At a glance
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| Question or attribute | Statistical significance | Practical significance |
|---|---|---|
| Main concern | Statistical-test interpretation | Real-world importance |
| Common information | P-value and testing criterion | Effect size and contextual meaning |
| What it cannot settle alone | Effect magnitude or importance | Strength of statistical evidence |
| Medical expression | Statistical significance | Clinical relevance |
These are complementary dimensions, not interchangeable labels. 1 2
What each thing is
Statistical significance belongs to the interpretation of statistical tests. Practical significance concerns whether a difference or relationship has meaningful consequences outside the analysis. The medical source discusses this second question as clinical relevance; that terminology reflects its healthcare setting, rather than defining every possible practical context. 1
Key differences
The central boundary is evidence versus importance. A p-value does not measure effect size, and statistical significance does not measure a result’s importance. Effect sizes quantify magnitude, but their practical interpretation still requires context. Moreover, the ASA cautions that a p-value alone is not an adequate measure of evidence about a model or hypothesis. 1 2
How to tell them apart
Ask what supports the claim. If the explanation rests on a statistical test or p-value threshold, it concerns statistical significance. If it explains the effect’s magnitude and why that difference matters in the relevant setting, it concerns practical significance. This identification rule has a limit: a numerical effect size alone does not supply the contextual explanation. 1 2
Where they overlap
Both judgments can contribute to interpreting the same finding. The medical source advocates combining statistical analysis with effect sizes, confidence intervals, and clinical interpretation. The overlap is therefore an interpretive task: assessing statistical results while also explaining the magnitude and relevance of the observed effect. One judgment does not replace the other. 1
Edge cases
An apparently important effect can still leave substantial uncertainty about the finding. In that situation, potential practical importance should not be presented as a settled conclusion. Conversely, a statistically significant result still needs an account of practical meaning. Considering estimates and intervals helps keep magnitude and uncertainty visible rather than collapsing interpretation into one label. 1 2
Why the distinction exists
The distinction prevents a statistical threshold from becoming a substitute for scientific reasoning. The ASA describes publication practices that favor statistically significant findings and warns against threshold-only conclusions. Separating statistical significance from practical significance keeps the question of real-world importance visible even when a test has produced a clear categorical label. 2
Common misconceptions
“Significant” does not automatically mean substantial, useful, or clinically meaningful. Nor does adding an effect-size label settle importance: the medical source presents conventional Cohen’s d benchmarks, but also calls for clinical interpretation. Such benchmarks should not be treated here as universal practical cutoffs, and a p-value should not be treated as a complete evidence summary. 1 2
Examples
Consider two explicitly hypothetical depression-treatment studies. In the first, a statistically significant score difference is too small to have meaningful clinical impact: statistical significance without practical significance. In the second, a score difference is judged meaningful in context and accompanied by statistical support: both judgments apply. These cases extend the medication-versus-placebo scenario introduced by the medical source. 1