The Box-Cox Transformation is one method of transforming non-normal data, or data that can not be assumed normal, to meet a normal distribution and allow further capability analysis and hypothesis testing.
The term is named after statisticians George and David Cox which is a method that uses an exponent, Lambda, to transform the data. The value of Lambda is the power to which each data point is raised. Then a new (transformed) set of data is created and that transformed set of data is used in for statistical analysis.
Data Type Assumption:
When the data is not normally distributed, this could result in inaccuracies when calculating a z-score. This could result in an inaccurate representation of your process capability.
Control charts may depict a process that is more or less in control than in reality; and, when performing a hypothesis tests the results of your tests may not be accurate, especially as the data is less normal.
Six Sigma Modules
Green Belt Program (1,000+ Slides)
Cause & Effect Matrix
Central Limit Theorem
1-Way Anova Test
Correlation and Regression