Some bigger picture stuff

  • Horoscopes (Richard McElreath), https://share.eva.mpg.de/index.php/s/9KEzTJg6oZ5dZZb

  • Writing research questions: Peters, M.A.K. How to develop good research questions. Nat Hum Behav (2025). https://doi.org/10.1038/s41562-025-02292-5 (a.k.a., read…a lot)

  • Scheel, A. M., Tiokhin, L., Isager, P. M., & Lakens, D. (2020). Why Hypothesis Testers Should Spend Less Time Testing Hypotheses. Perspectives on Psychological Science, 16(4), 744-755. https://doi.org/10.1177/1745691620966795

Writing about statistics

  • Basic Statistical Reporting for Articles Published in Biomedical Journals: The “Statistical Analyses and Methods in the Published Literature” or The SAMPL Guidelines” https://www.equator-network.org/wp-content/uploads/2013/03/SAMPL-Guidelines-3-13-13.pdf

  • Guidance on Statistical Reporting to Help Improve Your Chances of a Favorable Statistical Review https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7193848/pdf/rccm.202003-0477ED.pdf

  • Top tier guidance from a top rehab journal: Lohse, Keith R. PhD, PStat; Kliethermes, Stephanie PhD. Approaching Significance: Statistical Guidance for Authors and Reviewers. Journal of Neurologic Physical Therapy ():10.1097/NPT.0000000000000526, July 16, 2025. | DOI: 10.1097/NPT.0000000000000526

Common statistical errors I’ve come across in CSD research

  • Questionable interpretations of p-values Anderson, S. F. (2020). Misinterpreting p: The discrepancy between p values and the probability the null hypothesis is true, the influence of multiple testing, and implications for the replication crisis. Psychological Methods, 25(5), 596–609. https://doi.org/10.1037/met0000248

  • Many of the other NHST myths: Greenland, S., Senn, S. J., Rothman, K. J., Carlin, J. B., Poole, C., Goodman, S. N., & Altman, D. G. (2016). Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. European journal of epidemiology, 31(4), 337-350.

  • And questionable reporting of p-values - https://mchankins.wordpress.com/2013/04/21/still-not-significant-2/

  • The Difference Between “Significant” and “Not Significant” is not Itself Statistically Significant. Often known as, ‘yes you do need that interaction in your model’. - http://www.stat.columbia.edu/~gelman/research/published/signif4.pdf

  • Failing to report contrast coding or incorrect interpretation of contrasts Brehm, L., & Alday, P. M. (2022). Contrast coding choices in a decade of mixed models. Journal of Memory and Language, 125, 104334. https://doi.org/10.1016/j.jml.2022.104334

  • A long list of common statistical myths: https://discourse.datamethods.org/t/reference-collection-to-push-back-against-common-statistical-myths/1787

  • Liddell, T. M., & Kruschke, J. K. (2018). Analyzing ordinal data with metric models: What could possibly go wrong?. Journal of Experimental Social Psychology, 79, 328-348. and https://media.dlib.indiana.edu/media_objects/mp48sh50s

  • The Table 2 Fallacy: Westreich, D., & Greenland, S. (2013). The table 2 fallacy: presenting and interpreting confounder and modifier coefficients. American journal of epidemiology, 177(4), 292-298.

Data Management, Open Science & Reproducibility

  • A great resource with lots of information that could in many sections on this site. Frank, M. C., Braginsky, M., Cachia, J., Coles, N. A., Hardwicke, T. E., Hawkins, R. D., Mathur, M. B., & Williams, R. 2025. Experimentology: An Open Science Approach to Experimental Psychology Methods. Stanford University. https://doi.org/10.25936/3JP6-5M50. (Also published by MIT Press, ISBN 978-0-262-55256-1). free at: https://experimentology.io.

  • Lewis, C. (2024). Data Management in Large-Scale Education Research (1st ed.). Chapman and Hall/CRC. https://doi.org/10.1201/9781032622835 (free at: https://datamgmtinedresearch.com)

  • Strand, J. F. (2023). Error tight: Exercises for lab groups to prevent research mistakes. Psychological Methods.

  • Brown, V. A., & Strand, J. F. (2023). Preregistration: Practical Considerations for Speech, Language, and Hearing Research. Journal of Speech, Language, and Hearing Research, 66(6), 1889-1898.

  • Stanford Psychology guide to doing open science: https://poldrack.github.io/psych-open-science-guide/4_reproducibleanalysis.html

  • The Turing Way: https://book.the-turing-way.org

  • Alston, J. M., & Rick, J. A. (2021). A beginner’s guide to conducting reproducible research. Bulletin of the Ecological Society of America, 102(2), 1-14.

Blogs & Podcasts & People to follow on Socials (please add more!)

For me, a good blog post is the necessary on ramp to actually understanding a statistics paper and implementing an approach with my own data. Many of these writers/researchers are fantastic at distilling complex ideas into short(ish) blog posts that almsot anyone can understand.

  • Quantitude: https://quantitudepod.org

  • Solomon Kurz: https://solomonkurz.netlify.app/blog/

  • Andrew Heiss: https://www.andrewheiss.com/blog/

  • Danielle Navarro: https://djnavarro.net

  • Julia Rohrer & colleagues: https://www.the100.ci

  • Allison Horst: https://allisonhorst.com

  • Richard McElreath: https://xcelab.net/rm/

  • Jamie Reilly: https://www.reilly-coglab.com/reilly

  • Shravan Vasishth: https://vasishth.github.io

  • Julia Silge: https://juliasilge.com/blog/

  • Lisa Debruine: https://debruine.github.io

  • TJ Mahr: https://www.tjmahr.com/year-archive/

  • Michael Clark: https://m-clark.github.io/code.html

  • Daniel Lakens: http://daniellakens.blogspot.com

  • Gavin Simpson: https://fromthebottomoftheheap.net

  • Andrew Gelman: https://statmodeling.stat.columbia.edu

  • Kieran Healy: https://kieranhealy.org

  • Kristoffer Magnusson: https://rpsychologist.com/posts

Workshops (please add more!)

  • https://debruine.github.io/data-sim-workshops/

  • https://smart-workshops.com

  • https://centerstat.org

  • https://causalab.sph.harvard.edu/courses/

  • Annual statistics summer school: https://vasishth.github.io