How to Conduct a Simple Hypothesis Test in Six Sigma

I was teaching a Six Sigma Green Belt course in Washington, DC and was asked to simplify the basic road map in Hypothesis Testing, so I thought; this is a great idea to publish as a blog post so that more people can have access to this valuable information.

In the example below, we are testing whether or not there is a correlation between two continuous variables.

Hypothesis Test Example

1. What is the practical question?
– Does an increase in tire pressure cause an increase in tread wear?

2. What is the X? (Input being Controlled)
– Tire Pressure

3. What is the Y? (What is being Measured)
– Tread Wear

4. Gather Data, Run the Analysis and determine the Pearson Correlation Co-efficient and P-Value

5. Run a Correlation Analysis (selecting Pearson method). In this example the Pearson value ( r ) = 0.554 with a P-value = 0.0228

6. The results indicate that there is a medium correlation of the two factors and that the data set is significant at an Alpha risk level of .0228.

A guideline of R Values:

  • between -0.2 and +0.2 is random chance of a relationship
  • between -0.8 and -1.0 or +0.8 and +1.0 is a strong relationship
  • the remaining levels / values indicate there is a weak to moderate relationship

Remember that correlation is not causation. It is only a test of the strength of the linear relationship of two continuous factors; so the R Value indicates the strength of the relationship and the P Value indicates the Alpha Level the relationship can be stated to be significant.

We hope this simplifies the concept and serves as a guide to conduct a Hypothesis Test in Six Sigma. What problems (or opportunities) would you apply this to in your organization?

Share your thoughts in the comments below!