The New England Journal of Statistics in Data Science

Benjamin, D. J., Berger, J. O., Johannesson, M., Nosek, B. A., Wagenmakers, E-J., Berk, R., Bollen, K. A., Brembs, B., Brown, L., Camerer, C. et al.Redefine statistical significance. Nature Human Behaviour 2. 6–10 (2018).

Blocker, A. W. and Meng, X.-L. The potential and perils of preprocessing: Building new foundations. Bernoulli 19(4) 1176–1211 (2013). https://doi.org/10.3150/13-BEJSP16. MR3102548

Boyd, R. J., Powney, G. D. and Pescott, O. L. We need to talk about nonprobability samples (2022). arXiv preprint. arXiv:2210.07298.

Bradley, Valerie C., Kuriwaki, S., Isakov, M., Sejdinovic, D., Meng, X.-L. and Flaxman, S. Unrepresentative big surveys significantly overestimated US vaccine uptake. Nature 600(7890) 695–700 (2021).

Conklin, J. Dialogue mapping: Building shared understanding of wicked problems. John Wiley & Sons, Inc., (2005).

Cook-Deegan, R. M. The gene wars: Science, politics, and the human genome. WW Norton & Company, (1994).

Craiu, R. V., Gong, R. and Meng, X.-L. Six statistical senses. Annual Review of Statistics and Its Application. in press. https://doi.org/10.1146/annurevstatistics-040220-015348.

Dahleh, M. A. Data Science in Domains: Interaction of Physical, Social, and Institutional Systems. Harvard Data Science Review 3(2) (2021). https://hdsr.mitpress.mit.edu/pub/nv9xq9yu.

Dwork, C. Differential privacy: A survey of results. In International Conference on Theory and Applications of Models of Computation 1–19. Springer, (2008). https://doi.org/10.1007/978-3-540-79228-4_1. MR2472670.

Dwork, C. and Smith, A. Differential privacy for statistics: What we know and what we want to learn. Journal of Privacy and Confidentiality 1(2) 135–154 (2009). https://doi.org/10.1007/978-3-540-79228-4_1. MR2472670.

Gong, R., Groshen, E. L. and Vadhan, S. Harnessing the Known Unknowns: Differential Privacy and the 2020 Census. Harvard Data Science Review, (Special Issue 2), (2022). https://hdsr.mitpress.mit.edu/pub/fgyf5cne.

Haas, L., Hero, A. and Lue, R. A. Highlights of the National Academies Report on “Undergraduate Data Science: Opportunities and Options? Harvard Data Science Review 1(1) (2019). https://doi.org/10.1162/99608f92.38f16b68.

Junk, T. R. and Lyons, L. Reproducibility and replication of experimental particle physics results. Harvard Data Science Review 2(4) (2020). https://doi.org/10.1162/99608f92.250f995b.

Kifer, D. and Machanavajjhala, A. No free lunch in data privacy. In Proceedings of the 2011 International Conference on Management of Data – SIGMOD ’11, Athens, Greece 193–204 (2011). ACM Press. https://doi.org/10.1145/1989323.1989345.

Larson, E. J. The Myth of Artificial Intelligence. Harvard University Press, (2021).

Leonelli, S. Data Science in Times of Pan(dem)ic. Harvard Data Science Review 3(1) (2021). https://hdsr.mitpress.mit.edu/pub/fi1rol2i.

Li, X. and Meng, X.-L. A multi-resolution theory for approximating infinite-p-zero-n: Transitional inference, individualized predictions, and a world without bias-variance tradeoff. Journal of the American Statistical Association 116(533) 353–367 (2021). https://doi.org/10.1080/01621459.2020.1844210. MR4227699.

Lindsey, J. K. Some statistical heresies (with discussion). Journal of the Royal Statistical Society: Series D (The Statistician) 48(1) 1–40 (1999).

Meng, X.-L. Multiple-imputation inferences with uncongenial sources of input (with discussion). Statistical Science 9(4) 538–558 (1994). http://www.jstor.org/stable/10.2307/2246252.

Meng, X.-L. Double effort, not double blind! Technical Report 382, Department of Statistics, University of Chicago (1994).

Meng, X.-L. Desired and feared — what do we do now and over the next 50 years? The American Statistician 63(3) 202–210 (2009). https://doi.org/10.1198/tast.2009.09045. MR2750343.

Meng, X.-L. A trio of inference problems that could win you a Nobel prize in statistics (if you help fund it). In Past, Present, and Future of Statistical Science (Eds: Lin et. al.) CRC Press, (2014). MR2049935.

Meng, X.-L. Statistical paradises and paradoxes in big data (I): Law of large populations, big data paradox, and the 2016 US presidential election. Annals of Applied Statistics 12(2) 685–726 (2018). https://doi.org/10.1214/18-AOAS1161SF. MR3834282.

Meng, X.-L. Data science: An artificial ecosystem. Harvard Data Science Review 1(1), 7 (2019). https://doi.org/10.1162/99608f92.ba20f892.

Meng, X.-L. Building Data Science Infrastructures and Infrastructural Data Science. Harvard Data Science Review 3(2) (2021). https://hdsr.mitpress.mit.edu/pub/kdqoo5ax.

Meng, X.-L. Enhancing (publications on) data quality: Deeper data minding and fuller data confession. Journal of the Royal Statistical Society: Series A (Statistics in Society) 184(4) 1161–1175 (2021). https://doi.org/10.1111/rssa.12762.

Msaouel, P. The big data paradox in clinical practice. Cancer Investigation 40(7) 567–576 (2022). https://doi.org/10.1080/07357907.2022.2084621.

Qin, S. J. Data Science Education With Domain Knowledge and System Principles. Harvard Data Science Review 3(2) (2021). https://hdsr.mitpress.mit.edu/pub/8si074w9.

Rubin, D. B. Bayesianly justifiable and relevant frequency calculations for the applied statistician. The Annals of Statistics 12 1151–1172 (1984). https://doi.org/10.1214/aos/1176346785. MR0760681.

Stein, M. and Meng, X.-L. Report on 1995 IMS-ASA invited panel on Speeding the referee process. IMS Bulletin 24. 607–608 (1995).

Su, W. You are the best reviewer of your own papers: An owner-assisted scoring mechanism. Advances in Neural Information Processing Systems 34. 27929–27939 (2021).

Su, W. A truthful owner-assisted scoring mechanism (2022). arXiv preprint arXiv:2206.08149.