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Introduction Version

Pairwise one-way ANOVA

Deprecated TASK
Deprecated since the version : 0.3.1
This task is deprecated
Typing name :  TASK.gws_stats.PairwiseOneWayAnova Brick :  gws_stats v

Test that two groups have the same population mean

Compute the one-way ANOVA test for pairwise samples, from a given reference sample.

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. It is a parametric version of the Kruskal-Wallis test.

  • Input: a table containing the sample measurements, with the name of the samples.

  • Output: a table listing the one-way ANOVA F statistic, and the p-value for each pairwise comparison testing.

  • Config Parameters:

  • "column_names": The columns used for pairwise comparison. By default, the first three columns are used.

Note: 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 or the Alexander-Govern test although with some loss of power.

For more details, see https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.f_oneway.html


The input table


The output result




The names of column to pre-select for comparison. By default, the first 500 columns are used

Type : ListMaximum occurrences number : -1



The name of the column(s) to pre-select

Type : string



Set True if it is a text pattern (regular expression), False otherwise

Type : bool



The column used as reference for pairwise comparison. Only this column is compared with the others.

Type : string


OptionalAdvanced parameter

The key of the row tag (representing the group axis) along which one would like to compare each column. This parameter is not used if a `reference column` is given.

Type : string


OptionalAdvanced parameter

Adjust p-values for multiple tests.

Type : ListMaximum occurrences number : 1


OptionalAdvanced parameter

The method used to adjust (correct) p-values

Type : stringAllowed values : bonferroni  fdr_bh  fdr_by  fdr_tsbh  fdr_tsbky  sidak  holm-sidak  holm  simes-hochberg  hommel  Default value : bonferroni


OptionalAdvanced parameter

FWER, family-wise error rate. Default is 0.05

Type : floatDefault value : 0.05