In statistics, a significance level is the probability of rejecting the null hypothesis when it is actually true. In other words, it is the risk of making a Type I error. The significance level is typically set at 0.05, which means that there is a 5% chance of rejecting the null hypothesis when it is actually true.
However, there are times when it may be necessary to set a different significance level. For example, if the consequences of making a Type I error are very high, then it may be necessary to set a more stringent significance level, such as 0.01 or 0.001. Conversely, if the consequences of making a Type II error are very high, then it may be necessary to set a less stringent significance level, such as 0.10 or 0.20.
