Confidence Intervals in Six Sigma

Six Sigma Training and Certification is highly reliant on statistics.  Confidence intervals can be used to analyze data in a way that will enable Six Sigma organizational leaders and team members to reach conclusions in the form of percentages.

When a statistic is calculated, whether it be mean, variance, or proportion, there generally isn’t any reason to ensure that this estimate you’ve calculated is equal to the true population value. This is because you are working off a sample of a population.  This means that you are assuming  a certain number, or sample group, represents the entire population as a whole. Even when you increase the size of your sample, you must factor in the errors and inaccuracies that often occur.

In most of the projects where Six Sigma is used, there are some descriptive statistics that are calculated from sample data. This data does not necessarily accurately represent the true mean, variance, or proportion value of the population, but it can give team members a good idea of what these outcomes will be once a particular process has occurred.

Instead, it may be preferable to express an interval in which you would actually expect to find the true value of the population. This is what is called an interval estimate. A confidence interval is an interval that has been calculated from the sample data that will likely cover the unknown mean, variance or proportion.

Because of the inconsistencies that can develop from making such calculations, error must be factored into the equation. This is what is known as an error of estimation, margin of error, or standard error. This error is between the sample statistic and population value of that statistic. The confidence interval will define that margin of error.

Six Sigma methodology uses many different tools to reach important conclusions that can significantly improve overall organizational productivity. Confidence intervals are just one example of how useful these tools can be to all parts of an organization, which is why they have been implemented in many settings to test a wide range of data types. There are also various formulas that can be used for making different determinations using pieces of data that is collected for a specific purpose. This decision is dependent on specific situations so the best and most accurate results may be obtained.


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