Performs a 1-way ANOVA.

The one-way ANOVA tests the null hypothesis that two or more groups have the same population mean. The test is applied to samples from two or more groups, possibly with differing sizes.

Parameters :

sample1, sample2, ... : array_like

The sample measurements for each group.

Returns :

F-value : float

The computed F-value of the test.

p-value : float

The associated p-value from the F-distribution.


The ANOVA test has important assumptions that must be satisfied in order for the associated p-value to be valid.

  1. The samples are independent.
  2. Each sample is from a normally distributed population.
  3. The population standard deviations of the groups are all equal. This property is known as homoscedasticity.

If these assumptions are not true for a given set of data, it may still be possible to use the Kruskal-Wallis H-test (`stats.kruskal`_) although with some loss of power.

The algorithm is from Heiman[2], pp.394-7.


[R79]Lowry, Richard. “Concepts and Applications of Inferential Statistics”. Chapter 14.
[R80]Heiman, G.W. Research Methods in Statistics. 2002.

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