Which data analysis approach helps identify whether observed changes are meaningful in practice, not just statistically significant?

• A. P-values alone determine practical significance.
• B. Effect size indicates practical magnitude of change, not sample size; significance alone may be due to large samples.
• C. Confidence intervals alone determine practical importance.
• D. Descriptive statistics are sufficient.

Answer: (B)

When assessing whether observed changes matter in the real world, focus on practical significance—the size of the effect. The effect size tells you how large the change is in a standardized or absolute unit, so you can judge whether that change would have real impact in practice (for example, meaningful improvements in health outcomes, costs saved, or performance gains). P-values only tell you whether an observed difference could plausibly come from chance, and they can be misleading: with large samples, tiny differences can produce statistically significant results even if the real-world impact is negligible. Confidence intervals provide a range for the estimated effect and give a sense of precision, but they don’t by themselves communicate how meaningful the change is; you still need to know the magnitude to judge practical importance. Descriptive statistics describe the data but do not address whether the change is meaningful. So, focusing on the magnitude of the change through an effect size best identifies changes that matter in practice.