1. The problem involves understanding the significance level $0.01$ in hypothesis testing. 2. The significance level, denoted by $\alpha$, is the probability of rejecting the null hypothesis when it is actually true (Type I error). 3. A significance level of $0.01$ means there is a 1% risk of concluding that there is an effect when there is none. 4. This level is used to decide the critical value or rejection region in hypothesis tests. 5. For example, if testing assembly time for kitchen cabinets, $\alpha=0.01$ means we require strong evidence to reject the null hypothesis. 6. The smaller the $\alpha$, the stronger the evidence needed to reject the null hypothesis. 7. In summary, $0.01$ is a common significance level indicating a 1% chance of Type I error in hypothesis testing.