Elliot McClenaghan

Bayesian statistics has emerged as a powerful methodology for making decisions from data in the applied sciences. Bayesian brings a new way of thinking to statistics, in how it deals with probability, uncertainty and drawing inferences from an analysis.

In this article, we review the function of post-hoc tests in statistical analysis, how to interpret them and when to use them (and not use them).

The Wilcoxon signed rank test, which is also known as the Wilcoxon signed rank sum test and the Wilcoxon matched pairs test, is a non-parametric statistical test used to compare two dependent samples (in other words, two groups consisting of data points that are matched or paired). In this article, we explain how and when this test should be used.

The Kruskal–Wallis test is a statistical test used to compare two or more groups for a continuous or discrete variable. It is a non-parametric test, meaning that it assumes no particular distribution of your data and is analogous to the one-way analysis of variance (ANOVA). The Kruskal–Wallis test is sometimes referred to as the one-way ANOVA on ranks, or the Kruskal–Wallis one-way ANOVA.

The chi-squared test, often written as χ2 test, is a statistical hypothesis test used in the analysis of categorical variables to determine whether observed data are different from expectations. In this article, we explore the basics of this important test.

The Binomial test, sometimes referred to as the Binomial exact test, is a test used in sampling statistics to assess whether a proportion of a binary variable is equal to some hypothesized value.

The Mann-Whitney U Test, also known as the Wilcoxon Rank Sum Test, is a non-parametric statistical test used to compare two samples or groups. In this article, we explore the basics of the Test and work through an example.