friedman(data=None, dv=None, within=None, subject=None)
Friedman test for repeated measurements.
Name of column containing the dependent variable.
Name of column containing the within-subject factor.
Name of column containing the subject identifier.
'Q': The Friedman Q statistic, corrected for ties
'p-unc': Uncorrected p-value
'dof': degrees of freedom
The Friedman test is used for one-way repeated measures ANOVA by ranks.
Data are expected to be in long-format.
Note that if the dataset contains one or more other within subject factors, an automatic collapsing to the mean is applied on the dependent variable (same behavior as the ezANOVA R package). As such, results can differ from those of JASP. If you can, always double-check the results.
Due to the assumption that the test statistic has a chi squared distribution, the p-value is only reliable for n > 10 and more than 6 repeated measurements.
NaN values are automatically removed.
Compute the Friedman test for repeated measurements.
>>> from pingouin import friedman, read_dataset >>> df = read_dataset('rm_anova') >>> friedman(data=df, dv='DesireToKill', within='Disgustingness', ... subject='Subject') Source ddof1 Q p-unc Friedman Disgustingness 1 9.227848 0.002384