Statistical vs clinical significance

This video clarifies the difference between statistical and clinical significance in research.

In research, it’s important to distinguish between statistical significance and clinical significance when interpreting results.

Consider a study comparing Drug A and Drug B for reducing systolic blood pressure. The average blood pressure drops from 150 mmHg to 148 mmHg for those on Drug A. The p-value is 0.001, meaning the result is statistically significant. But does a 2 mmHg reduction meaningfully improve patients’ health?

Here’s the key difference:

• Statistical Significance (p-value < 0.05) indicates that the observed effect is unlikely due to chance.

• Clinical Significance determines whether the effect is large enough to impact patient outcomes and healthcare decisions.

Why Both Are Important?

A large sample size can make tiny differences statistically significant, but these differences may not translate into real-world benefits.

In the blood pressure study, a 2 mmHg reduction might not significantly lower heart disease or stroke risk, making it clinically insignificant.

Evaluating Clinical Significance

1. Effect Size – Larger effects are more likely to be clinically relevant.
2. Patient Outcomes – Does the effect lead to improved health, fewer hospitalizations, or reduced mortality?
3. Context & Population – What’s meaningful for one group (e.g., high-risk patients) may not be for others.

Striking the Right Balance

• Statistical significance helps determine if the results are real.

• Clinical significance assesses if they matter in practice.

• Confidence intervals provide further clarity on the reliability and importance of findings.