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# Repository Structure ## Analysis.ipynb This file is a Jupyter notebook. It contains all the code necessary to reproduce the figures and results reported in the main manuscript. To reproduce the analysis: 1. Clone the repository 2. Run conda env create --name outliers --file environment.yml to create a conda environment with all the required data and libraries. You will need to install the [Anaconda Distribution][2] first if you do not have it. 3. Activate the environment using conda activate outliers and run the notebook. ## Analysis.html This file is an export of the Jupyter notebook. It also contains all the figures, code, and results, but is not interactive. ## Literature Survey This folder contains the survey of the JEP:General paper containing the keyword "outlier" published in 2019 and 2020. ## Figures This subfolder contains the figures of the paper. ## Buffer 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 from scratch. * Buffered_pvals.zip contains the buffered p-values of the simulations. * 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. ## Data This subfolder contains the original data of Cao, Kong and Galinsky (2020), retrieved from [the OSF repository of the paper][1]. [1]: https://osf.io/e8xnz/ [2]: https://www.anaconda.com/distribution/