A Belt experienced an Alpha of .05 and a Beta of .10 and knew these are the most common risk levels when running a Statistical test.

The statement is true. In statistical hypothesis testing, Alpha (α) represents the probability of a Type I error (rejecting a true null hypothesis), and Beta (β) represents the probability of a Type II error (failing to reject a false null hypothesis). An Alpha level of 0.05, corresponding to a 95% confidence level, is the most widely used convention for significance. A Beta level of 0.10, which corresponds to a statistical power of 90% (Power = 1 - β), is also a very common and desirable target, especially in quality and process improvement contexts where failing to detect a real effect can be costly.

B. False: This option is incorrect because the values α = 0.05 and β = 0.10 are indeed standard, conventional risk levels used frequently in applied statistics, including the Six Sigma methodology, for conducting hypothesis tests.

1. Montgomery, D. C. (2020). Introduction to Statistical Quality Control (8th ed.). Wiley.

Page 131, Section 4-1.1: Discusses the choice of Alpha (α), stating, "The values 0.01, 0.05, and 0.10 are common," with 0.05 being the most prevalent standard.

Page 139, Section 4-1.4: In the context of determining sample size, the text notes, "In practice, one would specify a value for the power, say 0.90 or 0.95 (so β = 0.10 or 0.05)..." This confirms that a Beta of 0.10 is a standard choice for setting desired statistical power.

2. The Pennsylvania State University. (n.d.). STAT 502: Analysis of Variance and Design of Experiments, Lesson 2: Power and Sample Size. Penn State Eberly College of Science.

In the lesson on Power and Sample Size, it is noted that "By convention, α is often set to 0.05 and β to 0.20," but it clarifies that higher power (e.g., 0.90, meaning β = 0.10) is often desired. The combination of α=0.05 and power=0.90 (β=0.10) is a common objective in designing experiments.