References

Abelson, H., Sussman, G. J., & Sussman, J. (1996). Structure and interpretation of computer programs (2nd ed.). The MIT Press. https://mitpress.mit.edu/sites/default/files/sicp/
Allaire, J. J., Xie, Y., McPherson, J., Luraschi, J., Ushey, K., Atkins, A., Wickham, H., Cheng, J., Chang, W., & Iannone, R. (2022). rmarkdown: Dynamic documents for R. https://CRAN.R-project.org/package=rmarkdown
Anderson, J. R. (1990). The adaptive character of thought. Lawrence Erlbaum.
Anscombe, F. J. (1973). Graphs in statistical analysis. The American Statistician, 27(1), 17–21. https://doi.org/10.2307/2682899
Axelrod, R., & Hamilton, W. D. (1981). The evolution of cooperation. Science, 211(4489), 1390–1396. https://doi.org/10.1126/science.7466396
Bache, S. M., & Wickham, H. (2014). magrittr: A forward-pipe operator for R. https://CRAN.R-project.org/package=magrittr
Bar-Hillel, M. (1980). The base-rate fallacy in probability judgments. Acta Psychologica, 44(3), 211–233. https://doi.org/10.1016/0001-6918(80)90046-3
Bar-Hillel, M., & Falk, R. (1982). Some teasers concerning conditional probabilities. Cognition, 11(2), 109–122. https://doi.org/10.1016/0010-0277(82)90021-X
Bateman, S., Mandryk, R. L., Gutwin, C., Genest, A., McDine, D., & Brooks, C. (2010). Useful junk? The effects of visual embellishment on comprehension and memorability of charts. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2573–2582. https://doi.org/10.1145/1753326.1753716
Baumer, B. S., Kaplan, D. T., & Horton, N. J. (2021). Modern Data Science with R (2nd ed.). Chapman; Hall/CRC. https://mdsr-book.github.io/mdsr2e/
Behrens, J. T. (1997). Principles and procedures of exploratory data analysis. Psychological Methods, 2(2), 131–160. https://doi.org/10.1037/1082-989X.2.2.131
Bergstrom, C. T., & West, J. D. (2021). Calling bullshit: The art of skepticism in a data-driven world. Random House Trade Paperbacks. https://www.callingbullshit.org/
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. https://CRAN.R-project.org/package=bardr
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. https://doi.org/10.3389/fpsyg.2020.00750
Box, G. E. (1979). Robustness in the strategy of scientific model building. In Robustness in statistics (pp. 201–236). Elsevier. https://doi.org/10.1016/B978-0-12-438150-6.50018-2
Box, G. E. P. (1976). Science and statistics. Journal of the American Statistical Association, 71(356), 791–799. https://doi.org/10.1080/01621459.1976.10480949
Cairo, A. (2012). The functional art: An introduction to information graphics and visualization. New Riders.
Cairo, A. (2016). The truthful art: Data, charts, and maps for communication. New Riders.
Chang, W. (2012). R graphics cookbook: Practical recipes for visualizing data (2nd ed.). O’Reilly Media. https://r-graphics.org/
Craik, K. J. W. (1943). The nature of explanation. 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. https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century
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). College Publications. http://www.lib.uni-bonn.de/PhiMSAMP/Data/Book/PhiMSAMP-bk_DeCruzNethSchlimm.pdf
De Veaux, R. D., Agarwal, M., Averett, M., Baumer, B. S., Bray, A., Bressoud, T. C., Bryant, L., Cheng, L. Z., Francis, A., Gould, R., et al. (2017). Curriculum guidelines for undergraduate programs in data science. Annual Review of Statistics and Its Application, 4, 15–30. https://doi.org/10.1146/annurev-statistics-060116-053930
Donoho, D. (2017). 50 years of data science. Journal of Computational and Graphical Statistics, 26(4), 745–766. https://doi.org/10.1080/10618600.2017.1384734
Dudeney, H. E. (1917). Amusements in mathematics. Nelson; Sons. www.gutenberg.org/files/16713/16713.txt
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). Cambridge University Press. https://doi.org/10.1017/CBO9780511809477.019
Editorial. (1937). Mathematics and medicine. The Lancet. https://doi.org/10.1016/S0140-6736(00)86570-8
Editorial. (2009). The health illiteracy problem in the USA. The Lancet, 374, 2028. https://doi.org/10.1016/S0140-6736(09)62137-1
Erickson, T., Wilkerson, M., Finzer, W., & Reichsman, F. (2019). Data moves. Technology Innovations in Statistics Education, 12(1). https://doi.org/10.5070/T5121038001
Farrell, S., & Lewandowsky, S. (2010). Computational models as aids to better reasoning in psychology. Current Directions in Psychological Science, 19(5), 329–335. https://doi.org/10.1177/0963721410386677
Farrell, S., & Lewandowsky, S. (2018). Computational modeling of cognition and behavior. Cambridge University Press.
Frankfurt, H. G. (2009). On bullshit. Princeton 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. W.H. Freeman.
Gentner, D., & Stevens, A. L. (Eds.). (1983). Mental models. Lawrence Erlbaum Associates.
Gigerenzer, G. (2002). Reckoning with risk: Learning to live with uncertainty. Penguin.
Gigerenzer, G. (2004). Mindless statistics. The Journal of Socio-Economics, 33(5), 587–606. https://doi.org/10.1016/j.socec.2004.09.033
Gigerenzer, G. (2014). Risk savvy: How to make good decisions. Penguin.
Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Annual Review of Psychology, 62(1), 451–482. https://doi.org/10.1146/annurev-psych-120709-145346
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. https://doi.org/10.1111/j.1539-6053.2008.00033.x
Gigerenzer, G., & Hoffrage, U. (1995). How to improve Bayesian reasoning without instruction: Frequency formats. Psychological Review, 102(4), 684–704. https://doi.org/10.1037/0033-295X.102.4.684
Grolemund, G. (2014). Hands-on programming with R: Write your own functions and simulations. O’Reilly Media. https://rstudio-education.github.io/hopr/
Hacking, I. (1990). The taming of chance (Vol. 17). Cambridge University Press.
Hahn, U., & Warren, P. A. (2009). Perceptions of randomness: Why three heads are better than four. Psychological Review, 116(2), 454–461. https://doi.org/10.1037/a0015241
Hahn, U., & Warren, P. A. (2010). Why three heads are a better bet than four: A reply to Sun, Tweney, and Wang (2010). Psychological Review, 117(2), 706–711. https://doi.org/10.1037/a0019037
Healy, K. (2018). Data visualization: A practical introduction. Princeton University Press. https://socviz.co/
Herrnstein, R. J. (1991). Experiments on stable suboptimality in individual behavior. The American Economic Review, 81(2), 360–364. http://www.jstor.org/stable/2006885
Herrnstein, R. J., & Prelec, D. (1991). Melioration: A theory of distributed choice. The Journal of Economic Perspectives, 5(3), 137–156. http://www.jstor.org/stable/1942800
Herrnstein, R. J., & Vaughan, W., Jr. (1980). Melioration and behavioral allocation. In J. E. R. Staddon (Ed.), Limits to action: The allocation of individual behavior (pp. 143–176). Academic Press.
Hills, T. T., Todd, P. M., Lazer, D., Redish, A. D., Couzin, I. D., Cognitive Search Research Group, et al. (2015). Exploration versus exploitation in space, mind, and society. Trends in Cognitive Sciences, 19(1), 46–54. https://doi.org/10.1016/j.tics.2014.10.004
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). Lawrence Erlbaum.
Holte, R. C. (1993). Very simple classification rules perform well on most commonly used datasets. Machine Learning, 11(1), 63–90. https://doi.org/10.1023/A:1022631118932
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to statistical learning with applications in R (2nd edition). Springer. https://www.statlearning.com/
Johnson-Laird, P. N. (1983). Mental models: Towards a cognitive science of language, inference and consciousness. Cambridge University Press.
Kabacoff, R. (2018). Data visualization with R. Quantitative Analysis Center. https://rkabacoff.github.io/datavis/
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. https://doi.org/10.1016/0010-0285(72)90016-3
Kelion, L. (2020). Excel: Why using Microsoft’s tool caused Covid-19 results to be lost. BBC News, 2020-10-05. https://www.bbc.com/news/technology-54423988
Knuth, D. E. (1984). Literate programming. The Computer Journal, 27(2), 97–111. https://doi.org/10.1093/comjnl/27.2.97
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. https://doi.org/10.1037/0096-3445.132.1.3
Kuhn, M., & Johnson, K. (2013). Applied predictive modeling. Springer. http://appliedpredictivemodeling.com/
Kuhn, T. S. (1962). The structure of scientific revolutions. University of Chicago Press.
Kuleshov, V., & Precup, D. (2014). Algorithms for multi-armed bandit problems. arXiv Preprint arXiv:1402.6028. http://arxiv.org/abs/1402.6028
Lakatos, I., & Feyerabend, P. (1999). For and against method (M. Motterlini, Ed.). University of Chicago Press. https://doi.org/10.7208/9780226467030
Lattimore, T., & Szepesvári, C. (2020). Bandit algorithms. Cambridge University Press. https://tor-lattimore.com/downloads/book/book.pdf
Lewandowsky, S., & Farrell, S. (2011). Computational modeling in cognition: Principles and practice. SAGE publications.
Luce, R. D. (1995). Four tensions concerning mathematical modeling in psychology. Annual Review of Psychology, 46, 1. https://doi.org/10.1146/annurev.ps.46.020195.000245
Marr, D. (1982). Vision. A computational investigation into the human representation and processing of visual information. W.H. Freeman; Co.
Matloff, N. (2011). The art of R programming: A tour of statistical software design. No Starch Press.
McDowell, M., & Jacobs, P. (2017). Meta-analysis of the effect of natural frequencies on Bayesian reasoning. Psychological Bulletin, 143(12), 1273–1312. https://doi.org/10.1037/bul0000126
Miller, C. C. (2013). Data science: The numbers of our lives. New York Times. https://nyti.ms/146mC6R
Mosteller, F. (1965). Fifty challenging problems in probability with solutions. Addison-Wesley.
Müller, K. (2017). here: A simpler way to find your files. https://CRAN.R-project.org/package=here
Müller, K., & Wickham, H. (2021). tibble: Simple data frames. https://CRAN.R-project.org/package=tibble
Neth, H. (2022a). Data science for psychologists. Social Psychology; Decision Sciences, University of Konstanz. https://doi.org/10.5281/zenodo.7229812
Neth, H. (2022b). ds4psy: Data science for psychologists. Social Psychology; Decision Sciences, University of Konstanz. https://doi.org/10.5281/zenodo.7229812
Neth, H., Gaisbauer, F., Gradwohl, N., & Gaissmaier, W. (2022). riskyr: Rendering risk literacy more transparent. https://riskyr.org/, https://CRAN.R-project.org/package=riskyr
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. Wiley Online Library. https://doi.org/10.1002/9781118900772.etrds0394
Neth, H., & Gradwohl, N. (2022). unikn: Graphical elements of the university of konstanz’s corporate design. Social Psychology; Decision Sciences, University of Konstanz. https://doi.org/10.5281/zenodo.7096191
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. https://doi.org/10.3389/fpsyg.2020.567817
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. https://doi.org/10.1007/s11023-015-9368-8
Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2(2), 175–220. https://doi.org/10.1037/1089-2680.2.2.175
Nickerson, R. S. (2000). Null hypothesis significance testing: A review of an old and continuing controversy. Psychological Methods, 5(2), 241–301. https://doi.org/10.1037/1082-989X.5.2.241
Nowé, A., Vrancx, P., & De Hauwere, Y.-M. (2012). Game theory and multi-agent reinforcement learning. In M. Wiering & M. van Otterlo (Eds.), Reinforcement learning: State-of-the-art (pp. 441–470). Springer. https://doi.org/10.1007/978-3-642-27645-3_14
Page, S. E. (2018). The model thinker: What you need to know to make data work for you. Basic Books.
Pearl, J., & Mackenzie, D. (2018). The book of why: The new science of cause and effect. Basic Books.
Peng, R. D. (2011). Reproducible research in computational science. Science, 334(6060), 1226–1227. https://doi.org/10.1126/science.1213847
Peng, R. D. (2016). R programming for data science. Leanpub. https://bookdown.org/rdpeng/rprogdatascience/
Peng, R. D. (2020). Exploratory Data Analysis with R. Leanpub. https://bookdown.org/rdpeng/exdata/
Peng, R. D., & Hicks, S. C. (2020). Reproducible research: A retrospective. arXiv Preprint arXiv:2007.12210. https://arxiv.org/abs/2007.12210
Phillips, N. D. (2018). YaRrr! The pirate’s guide to R. https://bookdown.org/ndphillips/YaRrr/
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. http://journal.sjdm.org/17/17217/jdm17217.html
Pilkey, O. H., & Pilkey-Jarvis, L. (2007). Useless arithmetic: Why environmental scientists can’t predict the future. Columbia University Press.
R Core Team. (2022a). R base: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org
R Core Team. (2022b). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org
Rachlin, H., & Laibson, D. I. (Eds.). (1997). The matching law: Papers on psychology and economics by Richard Herrnstein. Russell Sage Foundation.
Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. In A. H. Black & W. F. Prokasy (Eds.), Classical conditioning II: Current research and theory (pp. 64–99). Appleton-Century-Crofts.
Ridsdale, C., Rothwell, J., Smit, M., Ali-Hassan, H., Bliemel, M., Irvine, D., Kelley, D., Matwin, S., & Wuetherick, B. (2015). Strategies and best practices for data literacy education: Knowledge synthesis report. Dalhousie University. http://hdl.handle.net/10222/64578
Ross, D. (2019). Game theory. In E. N. Zalta (Ed.), The stanford encyclopedia of philosophy (winter 2019 edition). https://plato.stanford.edu/archives/win2019/entries/game-theory/
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). Cognitive Science Society. http://nbn-resolving.de/urn:nbn:de:bsz:352-283870
Selvin, S. (1975). A problem in probability. American Statistician, 29, 67. https://doi.org/10.1080/00031305.1975.10479121
Silver, D., Singh, S., Precup, D., & Sutton, R. S. (2021). Reward is enough. Artificial Intelligence, 103535. https://doi.org/10.1016/j.artint.2021.103535
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. https://doi.org/10.1177/0956797611417632
Simon, H. A. (1956). Rational choice and the structure of the environment. Psychological Review, 63(2), 129–138. https://doi.org/10.1037/h0042769
Simon, H. A. (1996). The sciences of the artificial (3rd ed.). The MIT Press.
Sims, C. R., Neth, H., Jacobs, R. A., & Gray, W. D. (2013). Melioration as rational choice: Sequential decision making in uncertain environments. Psychological Review, 120(1), 139–154. https://doi.org/10.1037/a0030850
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. https://doi.org/10.1109/TVCG.2019.2940026
Sun, Y., Tweney, R. D., & Wang, H. (2010). Occurrence and nonoccurrence of random sequences: Comment on Hahn and Warren (2009). Psychological Review, 117(2), 697–705. https://doi.org/10.1037/a0018994
Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT press. http://incompleteideas.net/book/the-book.html
Szepesvári, C. (2010). Algorithms for reinforcement learning (Vol. 4). Morgan & Claypool. https://sites.ualberta.ca/~szepesva/rlbook.html
Szita, I. (2012). Reinforcement learning in games. In M. Wiering & M. van Otterlo (Eds.), Reinforcement learning: State-of-the-art (pp. 539–577). Springer. https://doi.org/10.1007/978-3-642-27645-3_17
Todd, P. M., Gigerenzer, G., & the ABC Research Group. (2012). Ecological rationality: Intelligence in the world. Oxford University Press.
Tufte, E. R. (2001). The visual display of quantitative information (2nd ed.). Graphics Press.
Tufte, E. R. (2006). Beautiful evidence (Vol. 1). Graphics Press.
Tufte, E. R., Goeler, N. H., & Benson, R. (1990). Envisioning information (Vol. 126). Graphics Press.
Tukey, J. W. (1969). Analyzing data: Sanctification or detective work. American Psychologist, 2, 83–91. https://doi.org/10.1037/h0027108
Tukey, J. W. (1977). Exploratory data analysis. Addison-Wesley.
Tukey, J. W. (1980). We need both exploratory and confirmatory. The American Statistician, 34(1), 23–25. https://www.jstor.org/stable/2682991
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131. https://doi.org/10.1126/science.185.4157.1124
Unwin, A. (2008). Good graphics? In Handbook of data visualization (pp. 57–78). Springer.
Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge University Press. https://doi.org/10.1017/CBO9780511815478
Wickham, H. (2014a). Advanced R (1st ed.). Chapman; Hall/CRC. http://adv-r.had.co.nz/
Wickham, H. (2014b). Tidy data. Journal of Statistical Software, 59(10), 1–23. https://doi.org/10.18637/jss.v059.i10
Wickham, H. (2019a). Advanced R (2nd ed.). Chapman; Hall/CRC. https://adv-r.hadley.nz/
Wickham, H. (2019b). tidyverse: Easily install and load the ’tidyverse’. https://CRAN.R-project.org/package=tidyverse
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T. L., Miller, E., Bache, S. M., Müller, K., Ooms, J., Robinson, D., Seidel, D. P., Spinu, V., … Yutani, H. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686
Wickham, H., Chang, W., Henry, L., Pedersen, T. L., Takahashi, K., Wilke, C., Woo, K., Yutani, H., & Dunnington, D. (2020). ggplot2: Create elegant data visualisations using the grammar of graphics. https://CRAN.R-project.org/package=ggplot2
Wickham, H., François, R., Henry, L., & Müller, K. (2021). dplyr: A grammar of data manipulation. https://CRAN.R-project.org/package=dplyr
Wickham, H., & Grolemund, G. (2017). R for data science: Import, tidy, transform, visualize, and model data. O’Reilly Media, Inc. http://r4ds.had.co.nz
Wickham, H., & Henry, L. (2020). tidyr: Tidy messy data. https://CRAN.R-project.org/package=tidyr
Wickham, H., Hester, J., & Bryan, J. (2022). readr: Read rectangular text data. https://CRAN.R-project.org/package=readr
Wilke, C. O. (2019). Fundamentals of data visualization: A primer on making informative and compelling figures. O’Reilly Media. https://clauswilke.com/dataviz/
Wu, K., Petersen, E., Ahmad, T., Burlinson, D., Tanis, S., & Szafir, D. A. (2021). Understanding data accessibility for people with intellectual and developmental disabilities. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3411764.3445743
Xie, Y. (2021). knitr: A general-purpose package for dynamic report generation in R. https://yihui.org/knitr/
Yau, N. (2011). Visualize this: The FlowingData guide to design, visualization, and statistics. John Wiley & Sons.
Yau, N. (2013). Data points: Visualization that means something. 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. https://doi.org/10.1038/sdata.2015.75