In this Introduction to Statistics module we will consider two characteristics of a process, the accuracy and the precision. It is important to be clear about the distinction between the two. If the process mean is close to the target then it is said to be 'accurate'. If the process has little variation it is said to be 'precise'.
A process can be accurate without being precise, and precise without being accurate. The target shows the results of ten shots from two different shooters:
| ACCURACY: the difference between the process mean and the target value |
The shooter on the right is most 'accurate'. Although the shots are widely scattered, the mean position is close to the target center.
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| PRECISION: the amount of variation, or 'scatter' |
The shooter on the left has least variation between shots and is thus more 'precise'.
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The mean of most processes can be adjusted fairly easily and most process improvement effort goes to reducing the amount of variation. Variation is the cause of most customer dissatisfaction, a customer who expects the salinity of a vial to be 50 will probably not be happy if the salinity is only 40; it may not work as well or at all. They will not care that the process average is 50.
Variation also makes a process hard to manage because it impacts the next step. A process that relies on the salinity level being correct may take longer, need to be reworked or even develop into a serious problem that requires extraordinary action.
The Six Sigma process improvement approach, used by many major companies, is named after the Greek letter σ , or 'sigma'. This is used in statistics to represent the 'standard deviation', the most common measure of variation. The Six Sigma approach aims to reduce variation to the stage where there are fewer than 3.4 defects for every million opportunities for that defect.
