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Shapiro-Wilk test

Description

The Shapiro-Wilk test compares a set of measures against the Normal distribution.

The test statistic is calculated as:

W = (SUM(a_ix_i))² / SUM(x_i - x-bar)

where: a_i is a constant based on the
x is the set of measures to assess, and x-bar is the mean of these measures.

If W is not small, then a Normal distribution may be concluded.

Shapiro-Wilk may be used before Parametric tests, to ensure the data being used has a Normal distribution.

Shapiro-Wilk is an improvement on the more general Kolmogorov-Smirnov curve-fitting algorithm.