<p>This file is a Jupyter notebook. It contains all the code necessary to reproduce the figures and results reported in the main manuscript.</p>
<p>To reproduce the analysis:
1. Clone the repository
2. Run <code>conda env create --name outliers --file environment.yml</code> to create a conda environment with all the required data and libraries. You will need to install the <a href="https://www.anaconda.com/distribution/" rel="nofollow">Anaconda Distribution</a> first if you do not have it.
3. Activate the environment using <code>conda activate outliers</code> and run the notebook.</p>
<p>This file is an export of the Jupyter notebook. It also contains all the figures, code, and results, but is not interactive.</p>
<p>This folder contains the survey of the JEP:General paper containing the keyword "outlier" published in 2019 and 2020.</p>
<p>This subfolder contains the figures of the paper.</p>
<p>This subfolder contains pre-computed data that would be computationally expensive to recompute. You can download and unzip these files if you want to use the pre-computed values: Otherwise, the code will re-generated them
<em> Buffered_pvals.zip contains the buffered p-values of the simulations.
</em> Traces_Across.zip and Traces_Within.zip contains the Bayesian traces of the simulations
* Traces_Summary_Across.csv and Traces_Summary_Within.csv contains the dataframes summarizing the Bayesian traces.</p>
<p>This subfolder contains the original data of Cao, Kong and Galinsky (2020),
retrieved from <a href="https://osf.io/e8xnz/" rel="nofollow">the OSF repository of the paper</a>.</p>