How does clinical significance differ from statistical significance?

Clinical significance refers to the practical importance or meaningfulness of a treatment's effects in real-world scenarios. It emphasizes whether the observed changes resulting from an intervention have substantial implications for the patient's functioning or quality of life. In essence, clinical significance looks at whether the results of a study can lead to meaningful benefits for the individuals receiving the treatment, evaluating whether those changes are sufficient to impact daily living, relationships, and overall well-being.

On the other hand, statistical significance pertains to the likelihood that the relationship or effect observed in the data is not due to chance, measured through p-values or confidence intervals. A result can be statistically significant without being clinically significant, meaning that while the data may indicate a change, that change may not be large enough to be relevant or impactful in practice.

The other options are less aligned with the distinction being examined. While sample size can impact statistical significance, it does not define clinical significance. Additionally, while statistical significance does often lack direct practical implications, that does not encompass the full scope of what clinical significance represents. Lastly, to state that there is no real difference between both terms overlooks the fundamental aspects that characterize each. Thus, focusing on real-life impacts is at the core of what defines clinical significance.