What does the term "sigma" represent in statistical terms?

The term "sigma" specifically refers to the standard deviation in statistical terms. It represents the amount of variation or dispersion in a set of values. In Six Sigma methodology, sigma is a crucial measurement that indicates the extent to which processes deviate from perfection, ultimately quantifying the level of defects.

A lower sigma level indicates a process with less variability and a higher level of performance, while a higher sigma level reflects greater variability and a greater likelihood of defects. This concept ties directly into Six Sigma's goal of reducing defects to improve quality. The focus on standard deviation allows organizations to understand their process capabilities and to strive for reducing variation, thus leading to better control and predictability in outcomes.

Other terms mentioned in the choices, like mean value, variance, and probability distribution, although essential in statistics, do not encapsulate the meaning of "sigma" in the context of Six Sigma. Mean value refers to the average of a dataset, variance measures the extent of variation between numbers in a dataset, and probability distribution describes how the values of a random variable are spread or arranged. However, none of these correspond to the concept of "sigma" as directly as standard deviation does.