The significance level or significance level (also known as alpha level) of a statistical test is the pre-selected probability of incorrectly rejecting the true null hypothesis. The level of significance also called the probability of type-I error which is Rejecting the null hypothesis when it is *true *type-I error is a false positive error*. *

Usually, a small value of significance level is chosen such as 0.01, 0.05, 0.1, etc and before doing statistical hypothesis testing, the maximum p-value for which the null hypothesis will be rejected is decided. This value is often denoted α (alpha) i.e. level of significance or significance level.

A 0.05 level of significance (5% significance level) means that there are 5% chances of making a wrong decision i.e. there are 5% chances that the true null hypothesis will be rejected, and there are 95% chances that the true null hypothesis is accepted.

If the p-value is less than the level of significance then the null hypothesis is rejected i.e. results of the hypothesis test are statistically significant. In the field of business studies, the level of significance or significance level is also known as the level of risk.