Analysis of variance

Statistics
Major approaches
Frequency probability
Bayesian inference
Non-parametric statistics
Common methods
Analysis of variance
Chi-Square test
Students t-test
Z test
Linear regression
Bayesian model selection
Bootstrapping

Analysis of variance or ANOVA is a statistical method for comparing different models. Models are built with explanatory parameters that attempt to describe the variance of a given data set. The amount of the variance explained by two models are compared and a corresponding value is calculated for how well one model explains the data relative to the other compared models. This value is called the f-value (named for R.A. Fisher who developed the first ANOVA model). The larger the f-value the better one model explains the variance than the others. In order to determine statistical significance the f-value is compared to a specific Fisher distribution based on the degrees of freedom. The Fisher distribution can give the probability that a given data set was derived from one model relative to another.

The one-way anova is probably one of the most used statistical methods for comparing data sets. Today most scientist calculate it using one of a range of statistical packages available.