This tool will perform a Wilcoxon signed-rank test to evaluate whether a significant statistical difference exists between the two rasters. The Wilcoxon signed-rank test is often used as a non-parametric equivalent to the paired-samples Student's t-test, and is used when the distribution of sample difference values between the paired inputs is non-Gaussian. The null hypothesis of this test is that difference between the sample pairs follow a symmetric distribution around zero. i.e. that the median difference between pairs of observations is zero.

The user must specify the name of the two input raster images (input1 and input2) and the output report HTML file (output). The test can be performed optionally on the entire image or on a random sub-sample of pixel values of a user-specified size (num_samples). In evaluating the significance of the test, it is important to keep in mind that given a sufficiently large sample, extremely small and non-notable differences can be found to be statistically significant. Furthermore statistical significance says nothing about the practical significance of a difference. Note that cells with a difference of zero are excluded from the ranking and tied difference values are assigned their average rank values.

See Also

paired_sample_test, two_sample_ks_test

Function Signature

def wilcoxon_signed_rank_test(self, raster1: Raster, raster2: Raster, output_html_file: str, num_samples: int) -> None: ...

Project Links

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