Metadata-Version: 2.1
Name: dabest
Version: 0.3.1
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
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Education
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Requires-Python: ~=3.6
Requires-Dist: numpy (~=1.19)
Requires-Dist: scipy (~=1.5)
Requires-Dist: pandas (~=1.1)
Requires-Dist: matplotlib (~=3.3)
Requires-Dist: seaborn (~=0.11)
Requires-Dist: lqrt (~=0.3)
Provides-Extra: dev
Requires-Dist: pytest (~=6.1) ; extra == 'dev'
Requires-Dist: pytest-mpl (~=0.11) ; extra == 'dev'

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


