Metadata-Version: 2.1
Name: dabest
Version: 0.2.1
Summary: Data Analysis and Visualization using Bootstrap-Coupled Estimation.
Home-page: https://acclab.github.io/DABEST-python-docs
Author: Joses W. Ho
Author-email: joseshowh@gmail.com
Maintainer: Joses W. Ho
Maintainer-email: joseshowh@gmail.com
License: BSD 3-clause Clear License
Download-URL: https://www.github.com/ACCLAB/DABEST-python
Platform: UNKNOWN

Estimation statistics is a simple framework <https://thenewstatistics.com/itns/>
that—while avoiding the pitfalls of significance testing—uses familiar statistical
concepts: means, mean differences, and error bars. More importantly, it focuses on
the effect size of one's experiment/intervention, as opposed to
significance testing.

An estimation plot has two key features. Firstly, it presents all
datapoints as a swarmplot, which orders each point to display the
underlying distribution. Secondly, an estimation plot presents the
effect size as a bootstrap 95% confidence interval on a separate but
aligned axes.

Please cite this work as:
Moving beyond P values: Everyday data analysis with estimation plots
Joses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, Adam Claridge-Chang
https://doi.org/10.1101/377978


