How does effect size complement p-values in interpreting study results?

How does effect size complement p-values in interpreting study results?

• A. Effect size quantifies the magnitude of an effect, providing practical significance beyond statistical significance.
• B. Effect size increases the p-value to reflect practical importance.
• C. Effect size replaces confidence intervals in interpretation.
• D. Effect size only matters in large samples.

Answer: (A)

The main idea is that effect size measures how big the observed effect is, giving practical significance beyond whether the result is statistically significant. A p-value tells you how likely the data would be if there were no real effect, but it says nothing about how large that effect is. So the two together give a fuller picture: you can have a result that is statistically significant but trivially small in magnitude (limited practical importance), or a large effect that isn’t statistically significant in a small study (suggesting the study may be underpowered). Effect size does not alter or increase the p-value, it simply communicates magnitude; it also doesn’t replace confidence intervals, which tell you about the precision of the estimated effect. And the magnitude of the effect matters in interpretation irrespective of sample size, even though precise estimates improve with larger samples.

The main idea is that effect size measures how big the observed effect is, giving practical significance beyond whether the result is statistically significant. A p-value tells you how likely the data would be if there were no real effect, but it says nothing about how large that effect is. So the two together give a fuller picture: you can have a result that is statistically significant but trivially small in magnitude (limited practical importance), or a large effect that isn’t statistically significant in a small study (suggesting the study may be underpowered). Effect size does not alter or increase the p-value, it simply communicates magnitude; it also doesn’t replace confidence intervals, which tell you about the precision of the estimated effect. And the magnitude of the effect matters in interpretation irrespective of sample size, even though precise estimates improve with larger samples.