Category: Normality

Six Sigma professionals should be familiar with normally distributed processes: the characteristic bell-shaped curve that is symmetrical about the mean, with tails approaching plus and minus infinity (Figure 1). When data fits a normal distribution, practitioners can make statements about the population using common analytical techniques, including control charts and capability indices (such […]

Normally distributed data is needed to use several statistical analysis tools, such as individual control charts, Cp/Cpk analysis, t-tests, and analysis of variance (ANOVA). When data is not normally distributed, the cause for non-normality should be determined and appropriate remedial actions should be taken. (An introduction to remedial actions for non-normal data can […]

What is non-normal data? Normally distributed data is a commonly misunderstood concept in Six Sigma. Some people believe that all data collected and used for analysis must be distributed normally. But normal distribution does not happen as often as people think, and it is not a main objective. Normal distribution is a means […]

The distribution of data can be categorized in two ways: normal and non-normal. If data is normally distributed, it can be expected to follow a certain pattern in which the data tend to be around a central value with no bias left or right (Figure 1). Non-normal data, on the other hand, does not tend […]

Most processes, particularly those involving life data and reliability, are not normally distributed. Most Six Sigma and process capability tools, however, assume normality. Only through verifying data normality and selecting the appropriate data analysis method will the results be accurate. This article discusses the non-normality issue and helps the reader understand some options for analyzing […]