Six Sigma process improvement strategies use two applications of statistics.  These are descriptive and inferential statistics.  Descriptive statistics describe the basic characteristics of a data set. It uses the data’s mean, median, mode, and standard deviation to create a picture of the behavior of the data.

 Inferential statistics uses descriptive statistics to infer qualities on a population, based on a sample from that population.  This involves making predictions. Examples of this are voter exit poling, sporting odds, and predicting customer behavior.

 Statistics are an important part of Six Sigma process improvement.  Even so, statistical calculations do not solve problems.  Business acumen and non-statistical tools are partners with statistical calculations in establishing root causes and in developing solutions.  As important as some sources tend to make statistical tools, Six Sigma improvement projects rarely fail because of math problems.  Instead, they fail due to a lack of honesty, management support, or a lack of business acumen.  The best screwdriver in the world will still make a poor pry bar.