Alpha and Beta Risks

Alpha risk is the risk of incorrectly deciding to reject the null hypothesis.

Beta risk is the risk that the decision will be made that the part is not defective when it really is. In other words, when the decision is made that a difference does not exist when there actually is.

If the confidence interval is 95%, then the alpha risk is 5% or 0.05. For example, there is a 5% chance that a part has been detemined defective when it actually is not. One has observed or made a decision that a difference exists but there really is none.

Alpha error is also called False Positive and Type I Error.

Confidence Level = 1 - Alpha

Throughout this site, an Alpha risk of 0.05 is assumed, making the confidence level = 95%.

Alpha is called the signficance level of a test. The level of significance may be 1% or 10% (or any value) depending on your desired level of confidence or need to reduce Type I error. Selecting 5% signifies that there is a 5% chance that the observed variation is not actually the truth.

If the power desired is 90%, then the Beta risk is 10%, or the risk is 10% that the decision will be made that the part is not defective when it reality it is defective. Or when the decision is made that a difference does not exist when there actually is.

The Power of a test = 1 - Beta

Beta error is also called False Negative and Type II Error.

The Power is the probability of correctly rejecting the Null Hypothesis.

The Null Hypothesis is technically never proven true. It is "failed to reject" or "rejected".

"Failed to reject" does not mean accept the null hypothesis since it is established only to be proven false by testing the sample of data. These test are covered in the ANALYZE phase.