BACKGROUND Although non-normal data are widespread in biomedical research, parametric tests unnecessarily predominate in statistical analyses. METHODS We surveyed five biomedical journals and - for all studies which contain at least the unpaired t-test or the non-parametric Wilcoxon-Mann-Whitney test - investigated the relationship between the choice of a statistical test and other variables ...

We present a case study of a difficult real-world pattern recognition problem: predicting hard drive failure using attributes monitored internally by individual drives. We compare the performance of support vector machines (SVMs), unsupervised clustering, and non-parametric statistical tests (rank-sum and reverse arrangements). Somewhat surprisingly, the rank-sum method outperformed the other m...

Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data. The results and inferences are precise only if proper statistical tests are used. This article will t...

BACKGROUND During the last 30 years, the median sample size of research studies published in high-impact medical journals has increased manyfold, while the use of non-parametric tests has increased at the expense of t-tests. This paper explores this paradoxical practice and illustrates its consequences. METHODS A simulation study is used to compare the rejection rates of the Wilcoxon-Mann-Whi...

Strandberg and Iglewicz (2012) propose a test that detects deviations from randomness, without an a priori distributional assumption. This nonparametric test is designed to detect deviations of neighboring observations from randomness, especially when the dataset consists of time series observations. The proposed test is especially effective for larger datasets. In our simulation study, this te...

We consider three general classes of data-driven statistical tests. Neyman’s smooth tests, data-driven score tests and data-driven score tests for statistical inverse problems serve as important special examples for the classes of tests under consideration. Our tests are additionally incorporated with model selection rules. The rules are based on the penalization idea. Most of the optimal penal...

Cognitive Science has typically proceeded with two major forms of research: model-building and experimentation. Traditional parametric statistics are normally used in the analysis of experiments, yet the assumptions required for parametric tests are almost never met in Cognitive Science. The purpose of this paper is twofold: to present a viable alternative to traditional parametric statistics—t...