What is defined as a statistical statement regarding the likelihood that a result occurred by chance?

Statistical significance refers to a determination that the observed results in a study are unlikely to have occurred by chance alone. This concept is crucial in research as it helps researchers draw conclusions about their findings based on the data they have collected. When results are statistically significant, it implies that the effect observed is likely due to a true underlying relationship rather than random variability.

To assess statistical significance, researchers commonly use p-values or confidence intervals, which provide a quantifiable measure of this likelihood. A commonly accepted threshold for significance is a p-value of less than 0.05, suggesting that there is less than a 5% probability that the results occurred by chance.

In contrast, the other terms—statistical reliability, effect size, and causal association—have different meanings. Statistical reliability refers to the consistency of a measure, effect size quantifies the magnitude of a relationship or difference, and causal association indicates a relationship where one factor directly affects another. While all these concepts are important in research, they do not specifically address the probability of results occurring by chance, which is why statistical significance is the correct choice here.