The Friedman test is used to detect any difference between subjects under test measured variously multiple times. More precisely, this non-parametric test states whether there is a significant difference in the location parameters of k statistical samples (>= 3, columns candidates, treatments, subject), measured n times (rows, blocks, participants, measures), or not. The data in each row is ranked, based on which a resulting test statistic Q is calculated.
If n > 15 or k > 4, the test statistic Q can be approximated to be Χ2 distributed. With the given significance level α, a corresponding p-value (null hypothesis H0: there is no difference of the location parameters in the samples, alternative hypothesis HA: the samples in the columns have different location parameters) can be given.
Please refer also to the Wikipedia description of the Friedman Test.
You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.
To use this node in KNIME, install the extension KNIME Statistics Nodes (Labs) from the below update site following our NodePit Product and Node Installation Guide:
A zipped version of the software site can be downloaded here.
Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.
Try NodePit Runner!Do you have feedback, questions, comments about NodePit, want to support this platform, or want your own nodes or workflows listed here as well? Do you think, the search results could be improved or something is missing? Then please get in touch! Alternatively, you can send us an email to mail@nodepit.com.
Please note that this is only about NodePit. We do not provide general support for KNIME — please use the KNIME forums instead.