Previous Journal Clubs

2023 Spring

Apr 11: What is the Problem? (1): Botvinik-Nezer, R., et al. (2020). Variability in the analysis of a single neuroimaging dataset by many teams. Nature, 582, 84–88. link

Apr 25: What is the Problem? (2): Gelman, A., & Loken, E. (2014). The statistical crisis in science. American Scientist, 102(6), 460. link

May 10: GitHub / Reproducible Code Workshop

May 23: Why is There a Problem? (3): Smaldino, P. E., & McElreath, R. (2016). The natural selection of bad science. Royal Society Open Science, 3(9), 160384. link

Jun 06: What is the Solution?: Munafò, M. R., et al. (2017). A manifesto for reproducible science. Nature Human Behaviour, 1(1), 0021. link

2024 Winter

Feb 12: MTurk: Too Good to Be True: Webb, A. A., & Tangney, J. P. (2022). Too good to be true: Bots and bad data from Mechanical Turk. Perspectives on Psychological Science. link

Feb 26: High Replicability of New Findings: Protzko, J., et al. (2023). High replicability of newly discovered social-behavioural findings is achievable. Nature Human Behaviour. link

2025 Winter

Mar 03: Registered Report: A Practical Discussion: Oshiro, B., Alley, L. J., & Flake, J. K. (2024). Want to try a registered report? Here are our lessons learned. Canadian Journal of Experimental Psychology.

2025 Fall

Oct 28: Critical Artificial Intelligence Literacy: Guest, O., & van Rooij, I. Critical artificial intelligence literacy for psychologists. link

Nov 20: Spill the Tea: Constructive Discussions of Obstacles to Open Science

2025 Spring

Apr 30: WEIRD Questions: How Researchers’ Bias Shapes Question Generation: Atari, M., Kroupin, I., Morhayim, L., Blasi, D. E., Schulz, J., & Henrich, J. (2025). WEIRD questions: Diversifying conceptual sampling.

May 28: Meta Analysis in the Open Science Era (Discussion + Workshop): Moreau, D., & Gamble, B. (2022). Conducting a meta-analysis in the age of open science: Tools, tips, and practical recommendations. Psychological Methods, 27(3), 426.