
Alpha risk is the risk of incorrectly deciding to reject the null hypothesis. 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 determined defective when it actually is not. One has observed or made a decision that a difference exists but there really is none. Or when the data on a control chart indicates the process is out of control but in reality the process is in control.
Alpha risk is also called False Positive and Type I Error.
Alpha is called the significance level of a test. The level of significance is commonly between 1% or 10% but can be 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.
The most common level for Alpha risk is 5% but it varies by application and this value should be agreed upon with your BB/MBB.
In summary, it's the amount of risk you are willing to accept of making a Type I error.
If
a carbon monoxide alarm goes off indicating a high level alert but
there is actually not a high level then this is Type I error.
If
conducting a 2sample T test and your conclusion is that the two means are
different when they are actually not would represent Type I error:
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. Or when the data on a control chart indicates the process is in control but in reality the process is out of control.
If the power desired is 90%, then the Beta risk is 10%.
There is a 10% chance that the decision will be made that the part is not defective when in reality it is defective.
Beta risk 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.
Guidelines: If the decision from the hypothesis test is looking for:
If conducting an Ftest and your conclusion is that the variances are the same when they are actually not would represent a Type II error.
Same note of caution as for Alpha, the assumption for Beta should be agreed upon with your BB/MBB.
If pvalue is < than alpharisk, reject Ho and accept the Alternative, Ha
If pvalue is > than alpharisk, fail to reject the Null, Ho
Try to rerun the test (if practical) to further confirm results. The next step is to take the statistical results and translate it to a practical solution.
It is also possible to determine the critical value of the test and use to calculated test statistic to determine the results. Either way, using the pvalue approach or critical value provides the same result.
Click here to purchase a presentation on Hypothesis Testing that explains more about the process and choosing levels of risk and power. Other topics within Six Sigma are also available. 
Return to the ANALYZE phase
Return to BASIC STATISTICS
Test your knowledge on Six Sigma certification questions
Return to SixSigmaMaterial Home Page
Six Sigma
Six Sigma Modules
The following presentations are available to download.
Green Belt Program (1,000+ Slides)
Basic Statistics
SPC
Process Mapping
Capability Studies
MSA
Cause & Effect Matrix
FMEA
Multivariate Analysis
Central Limit Theorem
Confidence Intervals
Hypothesis Testing
T Tests
1Way Anova Test
ChiSquare Test
Correlation and Regression
SMED
Control Plan
Kaizen
Error Proofing