Pairwise one-way ANOVA
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.
- "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.
- The samples are independent.
- Each sample is from a normally distributed population.
- 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 names of column to pre-select for comparison. By default, the first 500 columns are used
The name of the column(s) to pre-select
Set True if it is a text pattern (regular expression), False otherwise
The column used as reference for pairwise comparison. Only this column is compared with the others.
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.
Adjust p-values for multiple tests.
The method used to adjust (correct) p-values
FWER, family-wise error rate. Default is 0.05