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In statistics, a result is significant if it is unlikely to have occurred by chance, given that a presumed null hypothesis is true, but is not improbable if the null hypothesis is false.
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".
If the significance level is smaller, a value will be less likely to be more extreme than the critical value. So a result which is "significant at the 1% level" is more significant than a result which is "significant at the 5% level". However a test at the 1% level is more likely to have a Type II error than a test at the 5% level, and so will have less statistical power. In devising a hypothesis test, the tester will aim to maximize power for a given significance, but ultimately have to recognise that the best which can be achieved is likely to be a balance between significance and power, in other words between the risks of Type I and Type II errors.
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