Commonly used metrics are usually evaluated under the assumption that the data have a more-or-less normal distribution. However, often the data are actually skewed. Some metric expressions evaluated under the normality assumption can contain substantial errors. Normality is a special case for distributions in general.
Standard statistical software packages will often automatically generate key quantile values sufficient to aid in expressions that properly characterize distributions and accurately evaluate performance metrics. This leads to better evaluation of incremental process improvements. This article shows how quantile-based metric evaluations can be applied to all process specification variables to assess their relative performance. Sample size calculations are also more accurate when quantile-based expressions are used.