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

WbW Homepage User Manual Support WbW