It is difficult to establish the validity of any distributional assumption, and this is a common criticism of random effects meta-analyses.

Data envelopment analysis provides efficiency coefficients similar to those obtained by multivariate analysis without any distributional assumption.

In some cases, the distributional assumption relates to the observations themselves.

Test distributional assumptions of the (continuous) feature-distributions per category.

Both parametric and non-parametric tests were used because the distributional assumptions required for parametric testing may not be satisfied in all cases.

Many statistical analyses are based on distributional assumptions about the population from which the data have been obtained.

But first it is necessary to examine how the basic model developed in Sections 3 and 4 alters as a result of the changed distributional assumption.

The exact methods that do not make any distributional assumptions are referred to as exact nonparametric methods.

The latter has the advantage of making fewer assumptions whereas, the former tend to yield more powerful tests when the distributional assumption is reasonable.

Firstly, they need to be calibrated, introducing distributional assumptions.