Frequently used to detect excessive variation by comparing client requirements to tolerance limits that cover a specified proportion of the population. If the tolerance interval is wider than the client's requirements, there may be too much product variation. Derived from sample statistics, tolerance intervals are a range of values for a specific quality characteristic that likely covers a specified population proportion. Alternatively, a lower or upper limit may be constructed such that the specified proportion will be greater than or less than the limit.
To generate tolerance intervals, you must specify both a minimum percentage of the population and a confidence level. Traditionally, both values are close to 1. The percentage is the minimum population proportion you want the range to cover. The confidence level is the likelihood that the interval actually covers the minimum percentage.
For example, a parts manufacturer is interested in the width variability in one of their products. The analysts randomly sample 30 parts and record the width in millimeters (mm). Minitab's default tolerance interval states with 95% confidence that 95% of the population have width measurements falling within its bounds [5 8]. The manufacturer is 95% confident that 95% of all parts will have lengths falling between 5 and 8 mm. If this range is wider than their clients' requirements, the process may produce excessive waste.