Adding a Likelihood Q-Ratio Test about dabest-python HOT 4 CLOSED

@adam2392
Adam, I suggest using both the equal_var=True and equal_var=False flags for the unpaired Lq-likelihood-ratio-type test (LqRT), similarly to how it's done for the t-test here . syntax should be almost identical to the one of the scipy's t-test.

also, just as a clarification LqRT does not assume a gross-error model, it assumes normal distribution, but degrades less if there is contamination in the sample.

lastly, there is a preprint that discusses this package specifically, as opposed to the general LqRT; it can be found here.

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Hi @adam2392 ,

I'm happy to receive a PR that implements this additional test in the results dataframe. You will need to do a decent job of explaining the test in the documentation as it is obscure.

Do you have any examples of this test being used in biomedical research papers?

Joses

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Hi @josesho so in terms of the documentation, would this just be adding documentation into:

• tutorial.rst
• a release note
  ?

Or are there additional files to change regarding this.

Do you have any examples of this test being used in biomedical research papers?

No because it is a new publication, but the test is proven to be more robust (compared to the t-test) in terms of power for even small changes away from a perfectly Gaussian model, and it is better then the Wilcoxon rank-sum test in this aspect. So in terms of biomedical data, this would be nice because typically no data is perfectly modeled as a single Gaussian. I am using it though for my own research now as a result. The paper and pip package came out of my university, so I found out about it as soon as it was published.

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Closed with #89

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