Statistical heartburn: An attempt to digest four pizza publications from the Cornell Food and Brand Lab
- Subject Areas
- Ethical Issues, Science Policy, Statistics
- Statistics, Reproducibility, Replication, Reanalysis
- © 2017 van der Zee et al.
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2017. Statistical heartburn: An attempt to digest four pizza publications from the Cornell Food and Brand Lab. PeerJ Preprints 5:e2748v1 https://doi.org/10.7287/peerj.preprints.2748v1
We present the initial results of a reanalysis of four articles from the Cornell Food and Brand Lab based on data collected from diners at an Italian restaurant buffet. On a ﬁrst glance at these articles, we immediately noticed a number of apparent inconsistencies in the summary statistics. A thorough reading of the articles and careful reanalysis of the results revealed additional problems. The sample sizes for the number of diners in each condition are incongruous both within and between the four articles. In some cases, the degrees of freedom of between-participant test statistics are larger than the sample size, which is impossible. Many of the computed F and t statistics are inconsistent with the reported means and standard deviations. In some cases, the number of possible inconsistencies for a single statistic was such that we were unable to determine which of the components of that statistic were incorrect. We contacted the authors of the four articles, but they have thus far not agreed to share their data. The attached Appendix reports approximately 150 inconsistencies in these four articles, which we were able to identify from the reported statistics alone. We hope that our analysis will encourage readers, using and extending the simple methods that we describe, to undertake their own efforts to verify published results, and that such initiatives will improve the accuracy and reproducibility of the scientiﬁc literature.
This is a preprint submission to PeerJ Preprints.