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

Common statistical errors I’ve come across in CSD research

  • Questionable interpretations of p-values - https://pubmed.ncbi.nlm.nih.gov/31829657/

  • 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

  • But do your best to interpret interactions correctly - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0271668

  • Rohrer JM, Arslan RC. Precise Answers to Vague Questions: Issues With Interactions. Advances in Methods and Practices in Psychological Science. 2021;4(2). doi:10.1177/25152459211007368

  • Failing to report contrast coding or incorrect interpretation of contrasts - https://www.sciencedirect.com/science/article/abs/pii/S0749596X22000213

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

  • 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.

  • Ill-defined esimands:

    • Lundberg, I., Johnson, R., & Stewart, B. M. (2021). What is your estimand? Defining the target quantity connects statistical evidence to theory. American Sociological Review, 86(3), 532-565.
    • Kahan, B. C., Hindley, J., Edwards, M., Cro, S., & Morris, T. P. (2024). The estimands framework: a primer on the ICH E9 (R1) addendum. bmj, 384.

Open Science & Reproducibility

  • 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