In statistics, a result is **significant** if it is unlikely to have occurred by chance.

More precisely, in traditional frequentist statistical hypothesis testing, the **significance level** of a test is the maximum probability of accidentally rejecting a *true* null hypothesis (a decision known as a Type I error). The significance of a result is also called its **p**-value.

For example, one may choose a significance level of, say, 5%, and calculate a *critical value* of a statistic (such as the mean) so that the probability of it exceeding that value, given the truth of the null hypothesis, would be 5%. If the actual, calculated statistic value exceeds the critical value, then it is **significant** "at the 5% level".