Data Analysis and Visualization using Bootstrap-Coupled Estimation.
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
Release history Release notifications | RSS feed
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size dabest-0.3.0-py2.py3-none-any.whl (69.4 kB)||File type Wheel||Python version py2.py3||Upload date||Hashes View|
|Filename, size dabest-0.3.0.tar.gz (59.9 kB)||File type Source||Python version None||Upload date||Hashes View|
Hashes for dabest-0.3.0-py2.py3-none-any.whl