Choosing the correct hypothesis test is can be tricky as a new Six Sigma Green Belt or Black Belt.

There are several flowcharts and videos to help you determine the correct path. The assumption of normality is important to understand if you find your data to be non-normal. It may be possible to apply parametric tests even if your data is non-normal.

Two of the more common tests used are the t-test and z-test which begin to look similar as the sample size increase and represents more of the population. Visit the t-distribution for more insight.

T-tests are commonly used in Six Sigma projects as a hypothesis test for determining if:

- One mean from random sample is different than a target value.
- Two group means are different.
- Paired means are different. Such as a
*Before and After*study of the same process or workers.

The word * different* could be greater than, less than, or a certain value different than a target value. You can run statistical test in software usually be easily configuring the parameters to look for certain types of differences as were just mentioned.

For example, instead of just testing to see if one group mean is different than another, you can test to see if one group is a greater than the other and by a certain amount. You can get more information by adding more specific criteria to your test.

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