Although non-parametric tests have already been proposed for that purpose, statistical significance tests for non-standard measures (different from the classification error) are less often used in the literature. This paper is an attempt at empirically verifying how these tests compare with more classical tests, on various conditions. More precisely, using a very large dataset to estimate the w...

While performing statistical tests [7] the first question is which statistical test, parametric or non-parametric, is suitable. It is well known that parametric tests have much more power (probability that a statistical test will correctly reject a false null hypothesis, or a probability of avoiding a Type II error) than a non-parametric test. Hence, we should use parametric tests whenever allo...

Conventional statistical tests are usually called parametric tests. Parametric tests are used more frequently than nonparametric tests in many medical articles, because most of the medical researchers are familiar with and the statistical software packages strongly support parametric tests. Parametric tests require important assumption; assumption of normality which means that distribution of s...

statistical errors are common in scientific literature and about 50% of the published articles have at least one error. the assumption of normality needs to be checked for many statistical procedures, namely parametric tests, because their validity depends on it. the aim of this commentary is to overview checking for normality in statistical analysis using spss.

The importance of considering statistical power in marine pollution studies is unequivocal. However, the vast majority of ecological literature on power analysis focuses on parametric rather than non-parametric tests. This note describes a Monte Carlo simulation method for estimating the power of non-parametric tests. The method is illustrated using ordinal data.