What does statistical significance measure in the context of research findings?

Statistical significance is a fundamental concept in research that helps researchers understand whether their findings are likely to be genuine or if they could have occurred due to random chance. When researchers conduct experiments or analyses, they often compute a p-value, which indicates the probability that the observed results (or more extreme results) would occur if the null hypothesis were true—that is, if there were actually no effect or no difference.

A low p-value (often below a threshold like 0.05) suggests that the observed results are unlikely to have occurred if there were no true effect, which leads researchers to reject the null hypothesis in favor of the alternative. This implies that the findings are statistically significant, meaning they have a high likelihood of reflecting a real effect rather than results obtained through random variability in the data.

In contrast, the average score of a sample pertains to central tendency and does not provide insight into the reliability of the data. The variability of scores in a sample refers to how spread out the scores are, which is also distinct from assessing significance. Similarly, the strength of the relationship between variables involves correlation or causation measures, rather than the likelihood of findings occurring by chance. Therefore, statistical significance specifically focuses on the likelihood that results occur due to chance