References

Alter, U., Dang, C., Kunicki, Z. J., & Counsell, A. (2024). The VSSL scale: A brief instructor tool for assessing students’ perceived value of software to learning statistics. Teaching Statistics, 46(3). https://doi.org/10.1111/test.12374
Bakker, M., Veldkamp, C. L. S., Akker, O. R. van den, Assen, M. A. L. M. van, Crompvoets, E., Ong, H. H., & Wicherts, J. M. (2020). Recommendations in pre-registrations and internal review board proposals promote formal power analyses but do not increase sample size. PLoS ONE, 15(7), e0236079. https://doi.org/10.1371/journal.pone.0236079
Bartlett, J. E., Jenks, R., & Wilson, N. (2022). No Meaningful Difference in Attentional Bias Between Daily and Non-Daily Smokers. Journal of Trial & Error. https://doi.org/10.36850/e11
Bartlett, J., & Charles, S. (2022). Power to the People: A Beginner’s Tutorial to Power Analysis using jamovi. Meta-Psychology, 6. https://doi.org/10.15626/MP.2021.3078
Bem, D. J. (2011). Feeling the future: Experimental evidence for anomalous retroactive influences on cognition and affect. Journal of Personality and Social Psychology, 100(3), 407–425. https://doi.org/10.1037/a0021524
Binfet, J.-T., Green, F. L. L., & Draper, Z. A. (2022). The Importance of ClientCanine Contact in Canine-Assisted Interventions: A Randomized Controlled Trial. Anthrozoös, 35(1), 1–22. https://doi.org/10.1080/08927936.2021.1944558
Blanca, M. J., Alarcón, R., Arnau, J., Bono, R., & Bendayan, R. (2018). Effect of variance ratio on ANOVA robustness: Might 1.5 be the limit? Behavior Research Methods, 50(3), 937–962. https://doi.org/10.3758/s13428-017-0918-2
Champely, S. (2020). Pwr: Basic functions for power analysis. https://CRAN.R-project.org/package=pwr
Dasu, T., & Johnson, T. (2003). Exploratory data mining and data cleaning. Wiley-Interscience.
Dawtry, R. J., Sutton, R. M., & Sibley, C. G. (2015). Why Wealthier People Think People Are Wealthier, and Why It Matters: From Social Sampling to Attitudes to Redistribution. Psychological Science, 26(9), 1389–1400. https://doi.org/10.1177/0956797615586560
Evans, C., Cipolli, W., Draper, Z. A., & Binfet, J.-T. (2023). Repurposing a Peer-Reviewed Publication to Engage Students in Statistics: An Illustration of Study Design, Data Collection, and Analysis. Journal of Statistics and Data Science Education, 0(0), 1–21. https://doi.org/10.1080/26939169.2023.2238018
Hoffman, H. J., & Elmi, A. F. (2021). Do Students Learn More from Erroneous Code? Exploring Student Performance and Satisfaction in an Error-Free Versus an Error-full SAS® Programming Environment. Journal of Statistics and Data Science Education, 0(0), 1–13. https://doi.org/10.1080/26939169.2021.1967229
Irving, D., Clark, R. W. A., Lewandowsky, S., & Allen, P. J. (2022). Correcting statistical misinformation about scientific findings in the media: Causation versus correlation. Journal of Experimental Psychology. Applied. https://doi.org/10.1037/xap0000408
James, E. L., Bonsall, M. B., Hoppitt, L., Tunbridge, E. M., Geddes, J. R., Milton, A. L., & Holmes, E. A. (2015). Computer Game Play Reduces Intrusive Memories of Experimental Trauma via Reconsolidation-Update Mechanisms: Psychological Science, 26(8), 1201–1215. https://doi.org/10.1177/0956797615583071
Knief, U., & Forstmeier, W. (2021). Violating the normality assumption may be the lesser of two evils. Behavior Research Methods, 53(6), 2576–2590. https://doi.org/10.3758/s13428-021-01587-5
Lakens, D. (2022). Sample Size Justification. Collabra: Psychology, 8(1), 33267. https://doi.org/10.1525/collabra.33267
Lopez, A., Choi, A. K., Dellawar, N. C., Cullen, B. C., Avila Contreras, S., Rosenfeld, D. L., & Tomiyama, A. J. (2023). Visual cues and food intake: A preregistered replication of Wansink et al (2005). Journal of Experimental Psychology: General. https://doi.org/10.1037/xge0001503.supp
Nordmann, E., McAleer, P., Toivo, W., Paterson, H., & DeBruine, L. M. (2022). Data Visualization Using R for Researchers Who Do Not Use R. Advances in Methods and Practices in Psychological Science, 5(2), 25152459221074654. https://doi.org/10.1177/25152459221074654
Przybylski, A. K., & Weinstein, N. (2017). A Large-Scale Test of the Goldilocks Hypothesis: Quantifying the Relations Between Digital-Screen Use and the Mental Well-Being of Adolescents. Psychological Science, 28(2), 204–215. https://doi.org/10.1177/0956797616678438
R Core Team. (2024). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/
Weissgerber, T. L., Winham, S. J., Heinzen, E. P., Milin-Lazovic, J. S., Garcia-Valencia, O., Bukumiric, Z., Savic, M. D., Garovic, V. D., & Milic, N. M. (2019). Reveal, Don’t Conceal. Circulation, 140(18), 1506–1518. https://doi.org/10.1161/CIRCULATIONAHA.118.037777
Wickham, H. (2014). Tidy Data. Journal of Statistical Software, 59, 1–23. https://doi.org/10.18637/jss.v059.i10
Wickham, H. (2017). Tidyverse: Easily install and load the ’tidyverse’. https://CRAN.R-project.org/package=tidyverse
Wingen, T., Berkessel, J. B., & Englich, B. (2020). No Replication, No Trust? How Low Replicability Influences Trust in Psychology. Social Psychological and Personality Science, 11(4), 454–463. https://doi.org/10.1177/1948550619877412
Witt, J. K., Tenhundfeld, N. L., & Tymoski, M. J. (2018). Is there a chastity belt on perception? Psychological Science, 29(1), 139–146.
Woodworth, R. J., O’Brien-Malone, A., Diamond, M. R., & Schüz, B. (2018). Data from, Web-based Positive Psychology Interventions: A Reexamination of Effectiveness.” Journal of Open Psychology Data, 6(1), 1. https://doi.org/10.5334/jopd.35
Zhang, T., Kim, T., Brooks, A. W., Gino, F., & Norton, M. I. (2014). A Present for the Future: The Unexpected Value of Rediscovery. Psychological Science, 25(10), 1851–1860. https://doi.org/10.1177/0956797614542274