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

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