Ordinary statistical methods require the fulfillment of many assumptions about
distribution, linearity, etc. The MultiSimplex methods are free from such assumptions and
consequently easier to apply to most real world problems.
Statistical design of experiments is a powerful methodology for gaining scientific
insight, but to find optimum conditions it usually requires many more trials than the
MultiSimplex methods. Multicriteria optimization objectives are also more difficult to
apply.
