Statistical vs. Practical Significance Explained

Research Fundamentals #8


Statistical significance indicates whether an observed effect is unlikely to have occurred by chance, which is typically determined by a p-value (p < 0.05).

Key Features

Interpretation: A statistically significant result suggests strong evidence against the null hypothesis.
Limitations: Statistical significance does not indicate the strength or importance of an effect.

Example: A new medication reduces symptoms with p = 0.03, but the actual improvement is minimal.

Understanding Practical Significance


Practical significance assesses whether an effect is meaningful in real-world applications, often measured by effect size. We have to realize there is no actual cutoff score’s for whether communities, school districts, nonprofits, private companies etc. will adopt the insights into real world applications.

Key Features

Interpretation: A result can be statistically significant but practically insignificant if the effect size is too small to matter.
Effect Size Matters: Metrics like Cohen’s d, Pearson’s r, or odds ratios help quantify practical importance.

Example: A new teaching pedagogy claims to improve test scores by 1%, which is statistically significant but may not justify schoolwide budget and policy changes.

Statistical vs. Practical Significance: Key Differences

The below section combines the research skills you have learned thus far in determine quality of a research study,

Probability-based conclusion vs. Real-world impact assessment
Measure p-value, and Effect size (Cohen’s d, r, etc.)
Focus Rejection of null hypothesis Importance of effect
Decision Factor Sample size affects p-value Effect size determines meaningfulness

Why This Matters in Research

Overemphasis on p-values can lead to misleading conclusions.
Combining statistical and practical significance ensures meaningful and actionable research findings.
Researchers should report both to provide a complete interpretation of results.

Key Takeaways

Statistical significance helps determine if results are due to chance. Practical significance ensures that findings have meaningful, real-world implications. Reporting both measures provides a balanced interpretation of research results.




Leave a Comment

Your email address will not be published. Required fields are marked *