Analyzing nested experimental designs-A user-friendly resampling method to determine experimental significance.

ABSTRACT: 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. Resampling-based methods are a flexible strategy for performing these analyses but are difficult due to the lack of open-source software to automate test construction and execution. To address this, we present Hierarch, a Python package to perform hypothesis tests and compute confidence intervals on hierarchical 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 probabili

SUBMITTER: Kulkarni RU 

PROVIDER: S-EPMC9098003 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature