A variables sampling plan is a statistical procedure for making a pass/fail decision. When the sampling plan passes, there is an associated confidence statement relative to the spec limits that can be made like: "With 95% confidence, 99% of the values are in spec." Variables sampling plans make the pass/fail decision based on the capability indexes Pp and Ppk.

One has two options for using variables sampling plans. First, you can use tables of variables sampling plans to determine the acceptance criteria for Pp and Ppk. Distribution Analyzer then calculates and displays these capability indexes, allowing a pass/fail decision to be made. Second, you can state an acceptance criteria like "With 95% confidence more than 99% of values must be in spec." Then use Distribution Analyzer to construct the confidence statement relative to the spec limits to see if the study passes.

Variables sampling plans assume the underlying data fits the normal distribution. Before using one, you should pass a normality test. By default, the confidence statement relative to the spec limits is only displayed if one of the normality tests passes. For data that fits some other distribution, the data is first transformed along with the spec limits. Pp, Ppk and the confidence statement relative to the spec limits are then calculated using the transformed values.