Glossary

ANOVA (analysis of variance)

Definition

ANOVA (analysis of variance) tests whether the means of three or more groups differ. It splits the total variability of the data into the part explained by a factor (between groups) and the random part (within groups); their ratio yields the F statistic and a p-value. Factorial ANOVA additionally reveals interactions between factors.

Statistical tests

A significant ANOVA only says that at least one group differs — which ones is shown by post-hoc tests (Tukey, Scheffé). The assumptions are normal residuals and equal variances; when violated, a transformation or the nonparametric Kruskal–Wallis ANOVA helps.

Two-way and factorial ANOVA are the backbone of designed experiments: besides main effects (temperature, material) they test the interaction — whether the effect of one factor depends on the level of another. Repeated-measures ANOVA handles the case where the same units are measured several times.

In Statistica

The Statistics → ANOVA module offers one-way, factorial and repeated-measures ANOVA with a wizard that guides you from variable selection to plots of means with confidence intervals. For more complex designs (unbalanced data, covariates) use General Linear Models (GLM).

Related terms

Knowledgebase guides

FAQ

ANOVA is significant — am I done?
No. Post-hoc tests come next to tell which pairs of groups differ, plus an effect size (η²) to tell how much.
When should I use repeated-measures ANOVA?
When each unit is measured under several conditions or over time — e.g. patients before treatment, after a month and after a year.
What is an interaction between factors?
A situation where the effect of one factor depends on the level of another — a drug helps only younger patients. In a plot of means it appears as non-parallel lines.

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