References

Cavallo, Alberto, and Roberto Rigobon. 2016. “The Billion Prices Project: Using Online Prices for Measurement and Research.” Journal of Economic Perspectives 30 (2): 151–78. https://doi.org/10.1257/jep.30.2.151.
Chetty, Raj, and John N. Friedman. 2019. “A Practical Method to Reduce Privacy Loss When Disclosing Statistics Based on Small Samples.” AEA Papers and Proceedings 109 (May): 414–20. https://doi.org/10.1257/pandp.20191109.
Dekker, Sidney. 2014. The Field Guide to Understanding ’Human Error’. Third edition. Farnham, Surrey, England ; Burlington, VT, USA: Ashgate.
Gelman, Andrew, and Eric Loken. 2013. “The Garden of Forking Paths: Why Multiple Comparisons Can Be a Problem, Even When There Is No ‘Fishing Expedition’ or ‘p-Hacking’ and the Research Hypothesis Was Posited Ahead of Time.”
Gold, Alex K. 2024. DevOps for data science. First edition. Boca Raton, FL: CRC Press.
Ioannidis, John P. A. 2005. “Why Most Published Research Findings Are False.” PLoS Medicine 2 (8): e124. https://doi.org/10.1371/journal.pmed.0020124.
Knuth, D. E. 1984. “Literate Programming.” The Computer Journal 27 (2): 97–111. https://doi.org/10.1093/comjnl/27.2.97.
Nosek, Brian A., Charles R. Ebersole, Alexander C. DeHaven, and David T. Mellor. 2018. “The Preregistration Revolution.” Proceedings of the National Academy of Sciences 115 (11): 2600–2606. https://doi.org/10.1073/pnas.1708274114.
Parker, Hilary. n.d. “Opinionated Analysis Development.” https://doi.org/10.7287/peerj.preprints.3210v1.
Wickham, Hadley, Mine Çetinkaya-Rundel, and Garrett Grolemund. 2023. R for data science: import, tidy, transform, visualize, and model data. 2nd edition. Beijing Boston Farnham Sebastopol Tokyo: O’Reilly.
Wickham, Hadley, and Garrett Grolemund. 2016. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. First edition. Sebastopol, CA: O’Reilly.