TABLE OF CONTENTS Before beginning to collect data, a team must know the sample size. This section will discuss determining the sample size.
To prove that a process has been improved, the team must measure the process capability before and after improvements have been implemented. This measurement allows the team to quantify the process improvement (e.g., defect reduction or productivity improvement) and translate the effects into estimated financial results. Improved financial results are something that business leaders understand and appreciate. If data are not readily available for the process, the team must answer:
How many members of the population should be selected to ensure that the population is properly represented?
If the data have been collected, how would the team determine if it has enough data?
Determining sample size is an important issue because samples that are too large can waste time, resources, and money, while samples that are too small can lead to inaccurate results. In many situations, the minimum sample size is needed to estimate a process parameter, such as the population mean m.
Let, 
= Sample mean based on the collected data. This sample mean is generally different from the population mean m. The difference between the sample and population means can be considered to be an error. If E = margin of error, i.e., the maximum difference between the observed sample mean and the true value of the population mean m, then:
E = Z a/2
