Statistical significance, or level of significance, refers to 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 level of significance is typically set at 0.05, which means that there is a 5% chance of rejecting the null hypothesis when it is true.
There are several factors to consider when choosing a level of significance. One factor is the cost of making a Type I error. If the cost of making a Type I error is high, then a lower level of significance should be chosen. Another factor to consider is the cost of making a Type II error. A Type II error occurs when the null hypothesis is not rejected when it is actually false. The cost of making a Type II error can also be high, so it is important to consider both the cost of a Type I error and the cost of a Type II error when choosing a level of significance.