Why can a statistically significant result represent a very small effect?

A statistically significant result indicates that an observed difference is likely not due to chance, but it does not measure the magnitude, importance, or practical impact of that difference.1

A result can be statistically significant even if the effect size is very small because statistical significance is primarily a measure of whether an observation is distinguishable from random variance within a sample. When sample sizes are very large, even trivial differences between groups can produce a p-value low enough to reject the null hypothesis, leading researchers to conclude the result is "significant" despite the effect being practically negligible.12

Because statistical significance is not equivalent to clinical or practical significance, researchers must look beyond p-values to determine if an effect is meaningful in a real-world context. Relying solely on statistical significance without evaluating the size of the effect can lead to misleading conclusions about the importance of the findings.1