- Developing algorithms and making effect size statistics accessible.
- Nonoverlap proportion and the point-biserial association problem: Expository article published by ResearchOutreach.org.
- Nonoverlap proportion and the representation of point-biserial variation: In this paper, we discuss the fact that statistics methodology is subject to the general physical principle that it is necessary to account for all of the degrees of freedom when studying a quantitative phenomenon. We develop a computational framework for generating the statistical parameters for point-biserial variation.
- Factoring a 2 x 2 contingency table: In this paper, we show that a contingency table can be expressed as a product of marginal sum and proportion matrices. We also identify effect size measures for a 2 x 2 table that are invariant to variation in the marginal sums. The latter property is important for obtaining reproducible results in the analysis of categorical data.
- The National Academy of Sciences 2017 Colloqium on Reproducibility of Research: Issues and Proposed Remedies

The idea that statistical significance should be abandoned has received a lot of attention recently but it is not new, and efforts to replace p-value has been underway for some time. Perhaps the largest effort involves the development of effect size statistics. However, the large number of alternative effect size measures that have been proposed (Huberty, 2002) indicates that there are unresolved problems in this field as well. Only a small part of the large literature on these problems is cited here.

- Agresti, Alan. (2001). "Exact inference for categorical data: recent advances and continuing controversies". Statistics in Medicine. 20 (17-18): 2709–2722. doi:10.1002/sim.738
- Cumming, Geoff. (2012). Understanding The New Statistics. New York, NY: Routledge. doi:10.4324/9780203807002
- Gelman, Andrew; Loken, Eric. (2014). "The Statistical Crisis in Science". American Scientist. 102 (6): 460. DOI: 10.1511/2014.111.460
- Goodman, Leo A.; Kruskal, William H. (1954). "Measures of Association for Cross Classifications". Journal of the American Statistical Association. 49 (268): 732–764. doi:10.2307/2281536
- Grissom, Robert J.; Kim, John J. (2012). Effect Sizes for Research: Univariate and Multivariate Applications. (2nd ed.). New York, NY: Routledge/Taylor & Francis Group. doi:10.4324/9780203803233
- Hand, David J. (2006). "Classifier Technology and the Illusion of Progress." Statistical Science. 21 (1): 1–14. doi:10.1214/088342306000000060
- Hedrick, Philip W. (1987). "Gametic disequilibrium measures: proceed with caution". Genetics. 341 (2):, 331-341.
- Huberty, Carl J. (2002). "A History of Effect Size Indices". Educational and Psychological Measurement. 62 (2): 227-240. doi:10.1177/0013164402062002002
- Kelley, Ken, and Kristopher J. Preacher. (2012). “On Effect Size.” Psychological Methods 17 (2): 137–52. doi:10.1037/a0028086
- Leek, Jeff.; McShane, Blakeley B.; Gelman, Andrew; Colquhoun, David; Nuijten, Michèle B.; Goodman, Steven N. (2017). "Five ways to fix statistics". Nature. 551 (7682): 557-559. doi:10.1038/d41586-017-07522-z
- McCloskey, Deirdre; Ziliak, Steve. (2008). The Cult of Statistical Significance. Ann Arbor, MI: University of Michigan Press. doi:10.3998/mpub.186351
- Olivier, Jake; Bell, Melanie L. (2013). "Effect sizes for 2x2 contingency tables". PLoS ONE. 8 (3): e58777. doi:10.1371/journal.pone.0058777
- Nakagawa, Shinichi; Cuthill, Innes C. (2007). "Effect size, confidence interval and statistical significance: a practical guide for biologists". Biological Reviews of the Cambridge Philosophical Society. 82 (4): 591–605. doi:10.1111/j.1469-185X.2007.00027.x
- Stark, Philip B.; Saltelli, Andrea. (2018). “Cargo-Cult Statistics and Scientific Crisis”. Significance 15 (4): 40–43. doi:10.1111/j.1740-9713.2018.01174.x
- Warrens, Matthijs J. (2008). "On Association Coefficients for 2 x 2 Tables and Properties That Do Not Depend on the Marginal Distributions". Psychometrika. 73 (4): 777-789. doi:10.1007/s11336-008-9070-3
- Wasserstein, Ronald L.; Lazar, Nicole A. (2016) "The ASA's Statement on p-Values: Context, Process, and Purpose". The American Statistician. 70(2): 129-133. doi:10.1080/00031305.2016.1154108