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Data Analysis and Visualization using Bootstrap-Coupled Estimation.

Project description

Estimation statistics is a simple framework <> 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

Project details

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Filename, size & hash SHA256 hash help File type Python version Upload date
dabest-0.1.6-py2.py3-none-any.whl (39.3 kB) Copy SHA256 hash SHA256 Wheel py2.py3 Oct 5, 2018
dabest-0.1.6.tar.gz (22.9 kB) Copy SHA256 hash SHA256 Source None Oct 5, 2018

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