Uploaded December 2020 | Updated September 2026, 1 week ago
In this short video I show you how to transform data for use in parametric tests. When data does not meet the assumption of normality, we can transform it using non-linear functions.
I state, though, that it is probably better to use distribution-independent (non-parametric) tests.
In the video, we generate data that not normally distributed and use the log and square root transformations to transform it.
The file is available at github.com/juanklopper/Coursera-Doing-clinical-research and at wolframcloud.com/obj/juan.klopper/Published/Data%20transformation%20for%20statistical%20analysis.nb
In this short video I show you how to transform data for use in parametric tests. When data does not meet the assumption of normality, we can transform it using non-linear functions.
I state, though, that it is probably better to use distribution-independent (non-parametric) tests.
In the video, we generate data that not normally distributed and use the log and square root transformations to transform it.
The file is available at github.com/juanklopper/Coursera-Doing-clinical-research and at wolframcloud.com/obj/juan.klopper/Published/Data%20transformation%20for%20statistical%20analysis.nb
