Allaire, J. J., Xie, Y., McPherson, J., Luraschi, J., Ushey, K., Atkins, A., … Iannone, R. (2020). rmarkdown: Dynamic documents for R. Retrieved from
Anonymous Editorial. (1937). Mathematics and medicine. The Lancet.
Bache, S. M., & Wickham, H. (2014). magrittr: A forward-pipe operator for R. Retrieved from
Bar-Hillel, M. (1980). The base-rate fallacy in probability judgments. Acta Psychologica, 44(3), 211–233.
Bar-Hillel, M., & Falk, R. (1982). Some teasers concerning conditional probabilities. Cognition, 11(2), 109–122.
Baumer, B. S., Kaplan, D. T., & Horton, N. J. (2021). Modern Data Science with R (2nd ed.). Retrieved from
Behrens, J. T. (1997). Principles and procedures of exploratory data analysis. Psychological Methods, 2(2), 131–160.
Bertin, J. (2011). Semiology of graphics: Diagrams, networks, maps (Vol. 1). ESRI Press.
Billings, Z. (2021). bardr: Complete works of William Shakespeare in tidy format. Retrieved from
Binder, K., Krauss, S., & Wiesner, P. (2020). A new visualization for probabilistic situations containing two binary events: The frequency net. Frontiers in Psychology, 11, 750.
Box, G. E. (1979). Robustness in the strategy of scientific model building. In Robustness in statistics (pp. 201–236).
Box, G. E. P. (1976). Science and statistics. Journal of the American Statistical Association, 71(356), 791–799.
Cairo, A. (2012). The functional art: An introduction to information graphics and visualization. Berkeley CA: New Riders.
Cairo, A. (2016). The truthful art: Data, charts, and maps for communication. Berkeley CA: New Riders.
Chang, W. (2012). R graphics cookbook: Practical recipes for visualizing data (2nd ed.). Retrieved from
Craik, K. J. W. (1943). The nature of explanation. Cambridge, UK: Cambridge University Press.
Davenport, T. H., & Patil, D. J. (2012). Data scientist: The sexiest job of the 21st century. Harvard Business Review, 90(5), 70–76. Retrieved from
De Cruz, H., Neth, H., & Schlimm, D. (2010). The cognitive basis of arithmetic. In B. Löwe & T. Müller (Eds.), PhiMSAMP. Philosophy of mathematics: Sociological aspects and mathematical practice (pp. 59–106). Retrieved from
De Veaux, R. D., Agarwal, M., Averett, M., Baumer, B. S., Bray, A., Bressoud, T. C., … others. (2017). Curriculum guidelines for undergraduate programs in data science. Annual Review of Statistics and Its Application, 4, 15–30.
Donoho, D. (2017). 50 years of data science. Journal of Computational and Graphical Statistics, 26(4), 745–766. Retrieved from
Eddy, D. M. (1982). Probabilistic reasoning in clinical medicine: Problems and opportunities. In D. Kahneman, P. Slovic, & A. Tversky (Eds.), Judgment under uncertainty (pp. 249–267).
Farrell, S., & Lewandowsky, S. (2010). Computational models as aids to better reasoning in psychology. Current Directions in Psychological Science, 19(5), 329–335.
Farrell, S., & Lewandowsky, S. (2018). Computational modeling of cognition and behavior. Campbridge, UK: Cambridge University Press.
Friendly, M. (2008). A brief history of data visualization. In Handbook of data visualization (pp. 15–56). Springer.
Gardner, M. (1988). Time travel and other mathematical bewilderments. New York, NY: W.H. Freeman.
Gentner, D., & Stevens, A. L. (Eds.). (1983). Mental models. Mahwah, NJ: Lawrence Erlbaum Associates.
Gigerenzer, G. (2002). Reckoning with risk: Learning to live with uncertainty. London, UK: Penguin.
Gigerenzer, G. (2004). Mindless statistics. The Journal of Socio-Economics, 33(5), 587–606.
Gigerenzer, G. (2014). Risk savvy: How to make good decisions. New York, NY: Penguin.
Gigerenzer, G., Gaissmaier, W., Kurz-Milcke, E., Schwartz, L. M., & Woloshin, S. (2007). Helping doctors and patients make sense of health statistics. Psychological Science in the Public Interest, 8(2), 53–96.
Gigerenzer, G., & Hoffrage, U. (1995). How to improve Bayesian reasoning without instruction: Frequency formats. Psychological Review, 102(4), 684–704.
Grolemund, G. (2014). Hands-on programming with R: Write your own functions and simulations. Retrieved from
Hahn, U., & Warren, P. A. (2009). Perceptions of randomness: Why three heads are better than four. Psychological Review, 116(2), 454–461.
Healy, K. (2018). Data visualization: A practical introduction. Retrieved from
Hills, T. T., Todd, P. M., Lazer, D., Redish, A. D., Couzin, I. D., Cognitive Search Research Group, & others. (2015). Exploration versus exploitation in space, mind, and society. Trends in Cognitive Sciences, 19(1), 46–54.
Hintzman, D. L. (1991). Why are formal models useful in psychology. In S. L. William E. Hockley (Ed.), Relating theory and data: Essays on human memory in honor of Bennet B. Murdock (pp. 39–56). Hillsdale, NJ: Lawrence Erlbaum.
Holte, R. C. (1993). Very simple classification rules perform well on most commonly used datasets. Machine Learning, 11(1), 63–90.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112). Retrieved from
Johnson-Laird, P. N. (1983). Mental models: Towards a cognitive science of language, inference and consciousness. Cambridge, UK: Cambridge University Press.
Kabacoff, R. (2018). Data visualization with R. Retrieved from
Kahneman, D., & Tversky, A. (1972a). On prediction and judgement. ORI Research Monographs, 1(12), 430–454.
Kahneman, D., & Tversky, A. (1972b). Subjective probability: A judgment of representativeness. Cognitive Psychology, 3(3), 430–454.
Kelion, L. (2020). Excel: Why using Microsoft’s tool caused Covid-19 results to be lost. BBC News, (2020-10-05). Retrieved from
Knuth, D. E. (1984). Literate programming. The Computer Journal, 27(2), 97–111.
Krauss, S., & Wang, X.-T. (2003). The psychology of the Monty Hall problem: Discovering psychological mechanisms for solving a tenacious brain teaser. Journal of Experimental Psychology: General, 132(1), 3–22.
Kuhn, M., & Johnson, K. (2013). Applied predictive modeling. Retrieved from
Kuhn, T. S. (1962). The structure of scientific revolutions. Chicago, IL: University of Chicago Press.
Lakatos, I., & Feyerabend, P. (1999). For and against method (M. Motterlini, Ed.).
Lattimore, T., & Szepesvári, C. (2020). Bandit algorithms. Retrieved from
Lewandowsky, S., & Farrell, S. (2011). Computational modeling in cognition: Principles and practice. Thousand Oaks, CA: SAGE publications.
Luce, R. D. (1995). Four tensions concerning mathematical modeling in psychology. Annual Review of Psychology, 46, 1.
Marr, D. (1982). Vision. A computational investigation into the human representation and processing of visual information. New York, NY: W.H. Freeman; Co.
McDowell, M., & Jacobs, P. (2017). Meta-analysis of the effect of natural frequencies on Bayesian reasoning. Psychological Bulletin, 143(12), 1273–1312.
Miller, C. C. (2013). Data science: The numbers of our lives. New York Times. Retrieved from
Mosteller, F. (1965). Fifty challenging problems in probability with solutions. Reading, MA: Addison-Wesley.
Müller, K. (2017). here: A simpler way to find your files. Retrieved from
Müller, K., & Wickham, H. (2021). tibble: Simple data frames. Retrieved from
Neth, H. (2021a). Data science for psychologists. Retrieved from
Neth, H. (2021b). ds4psy: Data science for psychologists. Retrieved from
Neth, H., Gaisbauer, F., Gradwohl, N., & Gaissmaier, W. (2021). riskyr: Rendering risk literacy more transparent. Retrieved from,
Neth, H., & Gigerenzer, G. (2015). Heuristics: Tools for an uncertain world. In R. Scott & S. Kosslyn (Eds.), Emerging trends in the social and behavioral sciences.
Neth, H., & Gradwohl, N. (2021). unikn: Graphical elements of the university of konstanz’s corporate design. Retrieved from
Neth, H., Gradwohl, N., Streeb, D., Keim, D. A., & Gaissmaier, W. (2021). Perspectives on the 2x2 matrix: Solving semantically distinct problems based on a shared structure of binary contingencies. Frontiers in Psychology, 11, 567817.
Neth, H., Sims, C. R., & Gray, W. D. (2016). Rational task analysis: A methodology to benchmark bounded rationality. Minds and Machines, 26(1-2), 125–148.
Nickerson, R. S. (2000). Null hypothesis significance testing: A review of an old and continuing controversy. Psychological Methods, 5(2), 241–301.
Page, S. E. (2018). The model thinker: What you need to know to make data work for you. New York, NY: Basic Books.
Pearl, J., & Mackenzie, D. (2018). The book of why: The new science of cause and effect. New York, NY: Basic Books.
Peng, R. D. (2011). Reproducible research in computational science. Science, 334(6060), 1226–1227.
Peng, R. D. (2016). R programming for data science. Retrieved from
Peng, R. D. (2020). Exploratory Data Analysis with R. Retrieved from
Peng, R. D., & Hicks, S. C. (2020). Reproducible research: A retrospective. arXiv Preprint arXiv:2007.12210. Retrieved from
Phillips, N. D. (2018). YaRrr! The pirate’s guide to R. Retrieved from
Phillips, N. D., Neth, H., Woike, J. K., & Gaissmaier, W. (2017). FFTrees: A toolbox to create, visualize, and evaluate fast-and-frugal decision trees. Judgment and Decision Making, 12(4), 344–368. Retrieved from
Pilkey, O. H., & Pilkey-Jarvis, L. (2007). Useless arithmetic: Why environmental scientists can’t predict the future. New York, NY: Columbia University Press.
R Core Team. (2021a). R: A language and environment for statistical computing. Retrieved from
R Core Team. (2021b). R base: A language and environment for statistical computing. Retrieved from
Savant, M. vos. (1990). Ask Marilyn. Parade Magazine, (September 9), p. 15.
Schlimm, D., & Neth, H. (2008). Modeling ancient and modern arithmetic practices: Addition and multiplication with Arabic and Roman numerals. In B. Love, K. McRae, & V. Sloutsky (Eds.), Proceedings of the 30th Annual Meeting of the Cognitive Science Society (pp. 2097–2102). Retrieved from
Selvin, S. (1975). A problem in probability. American Statistician, 29, 67.
Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22(11), 1359–1366.
Simon, H. A. (1956). Rational choice and the structure of the environment. Psychological Review, 63(2), 129–138.
Simon, H. A. (1996). The sciences of the artificial (3rd ed.). Cambridge, MA: The MIT Press.
Streeb, D., El-Assady, M., Keim, D., & Chen, M. (2019). Why visualize? Untangling a large network of arguments. IEEE Transactions on Visualization and Computer Graphics.
Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). Retrieved from
Szepesvári, C. (2010). Algorithms for reinforcement learning (Vol. 4). Retrieved from
Todd, P. M., Gigerenzer, G., & the ABC Research Group. (2012). Ecological rationality: Intelligence in the world. New York, NY: Oxford University Press.
Tufte, E. R. (2001). The visual display of quantitative information (2nd ed.). Cheshire, CT: Graphics Press.
Tufte, E. R. (2006). Beautiful evidence (Vol. 1). Cheshire, CT: Graphics Press.
Tufte, E. R., Goeler, N. H., & Benson, R. (1990). Envisioning information (Vol. 126). Cheshire, CT: Graphics Press.
Tukey, J. W. (1969). Analyzing data: Sanctification or detective work. American Psychologist, 2, 83–91.
Tukey, J. W. (1977). Exploratory data analysis. Reading, MA: Addison-Wesley.
Tukey, J. W. (1980). We need both exploratory and confirmatory. The American Statistician, 34(1), 23–25. Retrieved from
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131.
Unwin, A. (2008). Good graphics? In Handbook of data visualization (pp. 57–78). Springer.
Wickham, H. (2014a). Advanced R (1st ed.). Retrieved from
Wickham, H. (2014b). Tidy data. Journal of Statistical Software, 59(10), 1–23.
Wickham, H. (2015). R packages: Organize, test, document, and share your code. Retrieved from
Wickham, H. (2019a). Advanced R (2nd ed.). Retrieved from
Wickham, H. (2019b). tidyverse: Easily install and load the ’tidyverse’. Retrieved from
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., … Yutani, H. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686.
Wickham, H., Chang, W., Henry, L., Pedersen, T. L., Takahashi, K., Wilke, C., … Dunnington, D. (2020). ggplot2: Create elegant data visualisations using the grammar of graphics. Retrieved from
Wickham, H., François, R., Henry, L., & Müller, K. (2021). dplyr: A grammar of data manipulation. Retrieved from
Wickham, H., & Grolemund, G. (2017). R for data science: Import, tidy, transform, visualize, and model data. Retrieved from
Wickham, H., & Henry, L. (2020). tidyr: Tidy messy data. Retrieved from
Wickham, H., Hester, J., & Francois, R. (2018). readr: Read rectangular text data. Retrieved from
Wilke, C. O. (2019). Fundamentals of data visualization: A primer on making informative and compelling figures. Retrieved from
Xie, Y. (2021). knitr: A general-purpose package for dynamic report generation in R. Retrieved from
Yau, N. (2011). Visualize this: The FlowingData guide to design, visualization, and statistics. Hoboken, NJ: John Wiley & Sons.
Yau, N. (2013). Data points: Visualization that means something. Hoboken, NJ: John Wiley & Sons.
Yu, A. Z., Ronen, S., Hu, K., Lu, T., & Hidalgo, C. A. (2016). Pantheon 1.0, a manually verified dataset of globally famous biographies. Scientific Data, 3(1), 1–16.