DABEST is a package for Data Analysis using Bootstrap-Coupled ESTimation.
Estimation statistics is a simple framework that avoids the pitfalls of significance testing. It 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 a false dichotomy engendered by P values.
An estimation plot has two key features.
It presents all datapoints as a swarmplot, which orders each point to display the underlying distribution.
It presents the effect size as a bootstrap 95% confidence interval on a separate but aligned axes.
DABEST powers estimationstats.com, allowing everyone access to high-quality estimation plots.
DABEST has been tested on Python 2.7, 3.5, 3.6, and 3.7.
In addition, the following packages are also required:
To obtain these package dependencies easily, it is highly recommended to download the Anaconda distribution of Python.
You can install this package via
To install, at the command line run
pip install --upgrade dabest
You can also clone this repo locally (see intstructions here).
Then, navigate to the cloned repo in the command line and run
pip install .
Please refer to the documentation.
How to cite
Moving beyond P values: Everyday data analysis with estimation plots
Joses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, Adam Claridge-Chang
There is also a Matlab version of DABEST.
It is possible to use the R package
reticulate to run Python code. Please take a look at this tutorial on how to use
reticulate to analyse data in R.
To test DABEST, you will need to install pytest.
pytest in the root directory of the source distribution. This runs the test suite in the folder
dabest/tests. The test suite will ensure that the bootstrapping functions and the plotting functions perform as expected.
Please report any bugs on the Github issue tracker.
All contributions are welcome. Please fork the Github repo and open a pull request.