This information is adapted from and draws heavily on: Sheskin, David J. 2000. Handbook of Parametric and Nonparametric Statistical Procedures. Second Edition. Chapman & Hall/CRC, Boca Raton. 982 pp. I strongly recommend that you consult Sheskin for other statistical tests and measures of correlation. This is simply a short, incomplete description of some of the limitations and assumptions for ...

Classic parametric statistical significance tests, such as analysis of variance and least squares regression, are widely used by researchers in many disciplines, including psychology. For classic parametric tests to produce accurate results, the assumptions underlying them (e.g., normality and homoscedasticity) must be satisfied. These assumptions are rarely met when analyzing real data. The us...

After a brief historical survey of parametric survival models, from actuarial, biomedical, demographical and engineering sources, this paper discusses the persistent reasons why parametric models still play an important role in exploratory statistical research. The phase-type models are advanced as a flexible family of latent-class models with interpretable components. These models are now supp...

Assumption for 1 sample T test: Data are normally distributed. We have discussed in the last article on how to check the normality assumption of a quantitative data. One issue being highlighted was that these formal normality tests are very sensitive to the sample size of the variable concerned. As seen here, table II shows that the normality assumptions for both the systolic and diastolic bloo...

Assessing the assumption of multivariate normality is required by many parametric multivariate statistical methods, such as discriminant analysis, principal component analysis, MANOVA, etc. Here, we present an R package to asses multivariate normality. The MVN package contains three most widely used multivariate normality tests, including Mardia’s, Henze-Zirkler’s and Royston’s multivariate nor...

Likert items and scales are often used in human subject studies to measure subjective responses of subjects to the treatment levels. In the field of human-robot interaction (HRI), with few widely accepted quantitative metrics, researchers often rely on Likert items and scales to evaluate their systems. However, there is a debate on what is the best statistical method to evaluate the differences...