In statistical methods, the significance level is a value that is used as a criterion for rejecting a null hypothesis. It used by first looking at the differences between the experimental results and from there the null hypothesis would be determined. Then, we assume that the null hypothesis is true, the probability of the differences is computed by using statistical tools. If the computed probability is less than or equal to the value of the significance level, then you conclude that the null hypothesis should be rejected and that the results are statistically significant. Usually, a significance level of 1% or 5% is used.
The significance level determines the critical region of the hypothesis test. A significance level of 10% means that there is a 10% probability of rejecting the null hypothesis incorrectly.
