What term describes the likelihood that a difference observed in data is not due to chance?

Statistical significance refers to the likelihood that an observed effect or difference in data is not attributable to random variation alone. When a result is statistically significant, it indicates that the findings are likely to be meaningful and reflect true effects within the population being studied, rather than occurring purely by chance.

This concept is often quantified through p-values, where a smaller p-value suggests stronger evidence against the null hypothesis (which posits that there is no effect or difference). A common threshold for statistical significance is a p-value of less than 0.05, which implies that there is less than a 5% probability that the observed differences occurred by random chance.

In this context, other options such as statistical power focus on the probability of correctly rejecting a false null hypothesis, while confidence intervals provide a range within which true population parameters are expected to lie. Margin of error, on the other hand, quantifies the uncertainty in sampling estimates. While these terms are related to the analysis of data, they do not directly describe the concept of distinguishing meaningful differences from random chance, which is the essence of statistical significance.