ANalysis Of VAriance (ANOVA)

Analysis of variance (a.k.a. ANOVA) is used to compare the means of more than 2 groups. Unsurprisingly, the way ANOVA works is by comparing variances (hence the name Analysis of Variance…). Variables must be categorical, and will often be called factors. There are several designs for ANOVA, depending on the number of variables to be compared and on whether samples are measured several times during the course of an experiment. In this chapter, we’ll talk about:

  • one-way ANOVA (1 single variable/factor with more than 2 levels),
  • factorial design alias two-way ANOVA, three-way ANOVA, multi-way ANOVA (2 or more factors with 2 or more levels each),
  • repeated measures ANOVA (individuals of the sample are tested/measured several times during the course of an experiment).

One-way ANOVA is a parametric test designed to compare the means of three or more groups. The null hypothesis states that the means of all groups to be tested are equal. As usual, the test will return a p-value in the end, and you will be able to decide whether […]

One-way ANOVA

Whereas one-way ANOVA allows for comparison of three and more group means based on the different levels of a single factor, factorial design allows for comparison of groups based on several independent variables and their various levels. Thus, comparing data (such as tree size, egg size…) split in groups according […]

Factorial design (multi-way ANOVA)

Repeated measures ANOVA is a test that seems close to one-way ANOVA as it allows to check for differences between the means of three and more groups. The essential difference is that the groups are dependent (i.e. related). This means that the groups contains data or measurements from the same individuals. What differs in […]

Repeated Measures ANOVA