This tool will perform a paired-sample t-test to evaluate whether a significant statistical difference exists between the two rasters. The null hypothesis is that the difference between the paired population means is equal to zero. The paired-samples t-test makes an assumption that the differences between related samples follows a Gaussian distribution. The tool will output a cumulative probability distribution, with a fitted Gaussian, to help users evaluate whether this assumption is violated by the data. If this is the case, the wilcoxon_signed_rank_test should be used instead.

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.

## See Also

two_sample_ks_test, wilcoxon_signed_rank_test

## Function Signature

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

## Project Links

WbW Homepage User Manual Support WbW