What is the difference between statistical significance and practical significance?

• A. Statistical significance indicates results unlikely due to chance; practical significance concerns real-world importance or effect size
• B. Statistical significance is about effect size; practical significance is about p-values
• C. Statistical significance guarantees causality; practical significance does not relate to real-world importance
• D. Statistical significance means results are important in practice; practical significance means not meaningful

Answer: (A)

The key idea here is distinguishing what a p-value or statistical test tells you from what matters in the real world. Statistical significance means the observed effect is unlikely to have happened by chance under the null hypothesis, given your chosen threshold for “unlucky” (the alpha level). It does not say anything about whether the effect matters in real-world terms.

Practical significance is about the real-world importance of the finding, which is tied to the size of the effect and its implications. A result can be statistically significant with a tiny effect if the sample is very large, but that small effect might be meaningless in practice. Conversely, an effect could be practically important even if it isn’t statistically significant in a small study, due to limited power. So the best statement separates these ideas: statistical significance indicates results unlikely due to chance; practical significance concerns real-world importance or effect size. The other choices confuse the relationship between p-values, effect size, and real-world impact.