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Hierarchical resampling provides exact statistical tests for nested experimental designs by combining bootstrap resampling within experimental units with permutation testing at the randomization level. This approach maintains Type I error control while using all available information, unlike traditional methods that either pool inappropriately or discard useful data.
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While hierarchical experimental designs are near-ubiquitous in neuroscience and biomedical research, researchers often do not take the structure of their datasets into account while performing statistical hypothesis tests. We present Hierarch, a Python package for analyzing nested experimental designs. Using a combination of permutation resampling and bootstrap aggregation, Hierarch can be used to perform hypothesis tests that maintain nominal Type I error rates and generate confidence intervals that maintain the nominal coverage probability without making distributional assumptions about the dataset of interest.