Understanding Effect Size and Statistical Significance in Meta-Analysis

Introduction

In meta-analysis, numbers tell the story — but interpreting them requires understanding two key concepts: effect size and statistical significance.
Effect size quantifies how big the effect is, while statistical significance tells you whether the effect is likely real or due to chance.
Both are essential for drawing meaningful conclusions from pooled research.

1. What is Effect Size?

Effect size is a standardized measure of the strength or magnitude of an observed relationship or treatment effect.
Unlike p-values, effect size gives insight into practical importance, not just statistical probability.

Common Effect Size Measures in Meta-Analysis:

Mean Difference (MD) – Difference between average scores in two groups.
Standardized Mean Difference (SMD) – Mean difference adjusted for variations in measurement scales.
Odds Ratio (OR) – Odds of an outcome occurring in one group vs. another.
Risk Ratio (RR) – Probability of an outcome in the treatment group vs. control.
Hazard Ratio (HR) – Likelihood of an event occurring over time.

2. Why Effect Size Matters

Compares results across different studies, even with different scales.
Shows clinical relevance – A statistically significant result with a tiny effect size may not be worth acting on.
Supports decision-making – Larger effect sizes can justify policy or treatment changes.

3. What is Statistical Significance?

Statistical significance answers the question:
“Is the observed effect likely to have occurred by chance?”

Measured by:

p-value – Probability of observing the result (or more extreme) if the null hypothesis is true.
- p < 0.05 is often considered significant.
Confidence Interval (CI) – Range of values where the true effect likely lies.
- If a 95% CI for an OR does not cross 1.0, the result is considered significant.

4. The Relationship Between Effect Size and Statistical Significance

Large effect + Significant p-value → Strong evidence of a meaningful effect.
Small effect + Significant p-value → May be statistically true but clinically unimportant.
Large effect + Non-significant p-value → Could be due to small sample size; warrants further study.

Example:
A meta-analysis finds that a new therapy reduces pain scores by 0.3 SMD (small effect) but with p < 0.001.
Clinically, the benefit might be minimal despite being statistically significant.

5. Visualizing Effect Size in Meta-Analysis

Forest plots – Show each study’s effect size and CI alongside the pooled estimate.
Funnel plots – Help detect publication bias, which can distort effect size estimates.

6. Common Misinterpretations to Avoid

Assuming statistical significance means large effect — Not always true.
Ignoring confidence intervals — Wide intervals indicate uncertainty.
Over-relying on p-values alone — Effect size should always be reported.

Conclusion

Effect size and statistical significance are two sides of the same coin in meta-analysis.
Effect size tells you how much difference there is; statistical significance tells you whether that difference is likely real.
Interpreting them together ensures balanced, accurate, and actionable conclusions.

Meta Title: Understanding Effect Size and Statistical Significance in Meta-Analysis
Meta Description: Learn how to interpret effect size and statistical significance in meta-analysis for more accurate and clinically relevant conclusions.