Six Sigma Green Belt Certification Practice Exam

The term that describes the risk of a type I error in a hypothesis test is referred to as the level of significance. This concept is fundamental in statistics, where a type I error occurs when a true null hypothesis is incorrectly rejected. The level of significance, often denoted by alpha (α), indicates the threshold for determining whether the evidence is strong enough to reject the null hypothesis. For example, a common significance level used is 0.05, suggesting a 5% risk of committing a type I error.

Understanding the level of significance is crucial in hypothesis testing because it directly affects the conclusions drawn from the analysis. A lower level of significance reduces the risk of a type I error but may increase the risk of a type II error, which is the failure to reject a false null hypothesis—this balance is essential in rigorous statistical analysis and decision-making processes.

In contrast, power refers to the probability of correctly rejecting a false null hypothesis (type II error), while confidence level represents the percentage of times you can expect the interval to contain the true population parameter across numerous samples. Beta risk, on the other hand, relates specifically to the risk of type II error and does not address type I error, further clarifying why level of significance is the correct