Investigating lay evaluations of models

References

• Arkes, H. R., Dawes, R. M., & Christensen, C. (1986). Factors influencing the use of a decision rule in a probabilistic task. Organizational Behavior and Human Decision Processes, 37(1), 93–110. https://doi.org/10.1016/0749-5978(86)90046-4
   Web of Science ®Google Scholar
• Baack, S. (2011). A new style of news reporting: Wikileaks and data-driven journalism. In B. Rambatan & J. Johanssen (Eds.), Cyborg subjects: Discourse on digital culture (1st ed., pp. 113–122). Create Space Independent Publishing Platform.
   Google Scholar
• Bissoto, A., Fornaciali, M., Valle, E., & Avila, S. (2019). (De) Constructing bias on skin lesion datasets. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.
   Google Scholar
• Camerer, C. F., & Johnson, E. J. (1997). The process-performance paradox in expert judgment: How can experts know so much and predict so badly? In H. Arkes & J. Baron (Eds.), Research on judgment and decision making: Currents, connections, and controversies (1st ed., pp. 342–365). Cambridge University Press.
   Google Scholar
• Carrasco, M. (2011). Visual attention: The past 25 years. Vision Research, 51(13), 1484–1525. https://doi.org/10.1016/j.visres.2011.04.012
   PubMed Web of Science ®Google Scholar
• Chouldechova, A. (2017). Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big Data, 5(2), 153–163. https://doi.org/10.1089/big.2016.0047
   PubMed Web of Science ®Google Scholar
• Ciccione, L., & Dehaene, S. (2021). Can humans perform mental regression on a graph? Accuracy and bias in the perception of scatterplots. Cognitive Psychology, 128, 101406.
   PubMed Web of Science ®Google Scholar
• Cleveland, W. S., Diaconis, P., & McGill, R. (1982). Variables on scatterplots look more highly correlated when the scales are increased. Science (New York, N.Y.), 216(4550), 1138–1141.
   PubMed Web of Science ®Google Scholar
• Coddington, M. (2015). Clarifying journalism’s quantitative turn: A typology for evaluating data journalism, computational journalism, and computer-assisted reporting. Digital Journalism, 3(3), 331–348. https://doi.org/10.1080/21670811.2014.976400
   Web of Science ®Google Scholar
• Dawes, R. M. (1971). A case study of graduate admissions: Application of three principles of human decision making. American Psychologist, 26(2), 180–188. https://doi.org/10.1037/h0030868
   Web of Science ®Google Scholar
• Dawes, R. M. (1979). The robust beauty of improper linear models in decision making. American Psychologist, 34(7), 571–582. https://doi.org/10.1037/0003-066X.34.7.571
   Web of Science ®Google Scholar
• Dawes, R. M., Faust, D., & Meehl, P. E. (1989). Clinical versus actuarial judgment. Science (New York, N.Y.), 243(4899), 1668–1674.
   PubMed Web of Science ®Google Scholar
• De Baets, S., & Harvey, N. (2020). Using judgment to select and adjust forecasts from statistical models. European Journal of Operational Research, 284(3), 882–895. https://doi.org/10.1016/j.ejor.2020.01.028
   Web of Science ®Google Scholar
• Diab, D. L., Pui, S. Y., Yankelevich, M., & Highhouse, S. (2011). Lay perceptions of selection decision aids in US and non‐US samples. International Journal of Selection and Assessment, 19(2), 209–216. https://doi.org/10.1111/j.1468-2389.2011.00548.x
   Web of Science ®Google Scholar
• Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144(1), 114–126. https://doi.org/10.1037/xge0000033
   PubMed Web of Science ®Google Scholar
• Eastwood, J., Snook, B., & Luther, K. (2012). What people want from their professionals: Attitudes toward decision‐making strategies. Journal of Behavioral Decision Making, 25(5), 458–468. https://doi.org/10.1002/bdm.741
   Web of Science ®Google Scholar
• Ebert, R. J., & Kruse, T. E. (1978). Bootstrapping the security analyst. Journal of Applied Psychology, 63(1), 110–119. https://doi.org/10.1037/0021-9010.63.1.110
   Web of Science ®Google Scholar
• Einhorn, H. J. (1986). Accepting error to make less error. Journal of Personality Assessment, 50(3), 387–395. https://doi.org/10.1207/s15327752jpa5003_8
   PubMed Web of Science ®Google Scholar
• Gigerenzer, G. (2004). Mindless statistics. The Journal of Socio-Economics, 33(5), 587–606. https://doi.org/10.1016/j.socec.2004.09.033
   Google Scholar
• Goldberg, L. R. (1968). Simple models or simple processes? Some research on clinical judgments. The American Psychologist, 23(7), 483–496. https://doi.org/10.1037/h0026206
   PubMed Web of Science ®Google Scholar
• Goldberg, L. R. (1969). The search for configural relationships in personality assessment: The diagnosis of psychosis vs. neurosis from the MMPI. Multivariate Behavioral Research, 4(4), 523–535. https://doi.org/10.1207/s15327906mbr0404_7
   PubMed Web of Science ®Google Scholar
• Goldberg, L. R. (1976). Man versus model of man: Just how conflicting is that evidence? Organizational Behavior and Human Performance, 16(1), 13–22. https://doi.org/10.1016/0030-5073(76)90003-9
   Web of Science ®Google Scholar
• Grove, W. M., Zald, D. H., Lebow, B. S., Snitz, B. E., & Nelson, C. (2000). Clinical versus mechanical prediction: a meta-analysis. Psychological Assessment, 12(1), 19–30.
   PubMed Web of Science ®Google Scholar
• Hammond, K. R., Hursch, C. J., & Todd, F. J. (1964). Analyzing the components of clinical inference. Psychological Review, 71(6), 438–456.
   PubMed Web of Science ®Google Scholar
• Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning (Vol. 1, pp. 241–249). Springer series in statistics.
   Google Scholar
• Hoekstra, R., Kiers, H., & Johnson, A. (2012). Are assumptions of well-known statistical techniques checked, and why (not)? Frontiers in Psychology, 3, 1–9. https://doi.org/10.3389/fpsyg.2012.00137
   PubMed Web of Science ®Google Scholar
• Hoekstra, R., Morey, R. D., Rouder, J. N., & Wagenmakers, E. J. (2014). Robust misinterpretation of confidence intervals. ).Psychonomic Bulletin & Review, 21(5), 1157–1164. https://doi.org/10.3758/s13423-013-0572-3
   PubMed Web of Science ®Google Scholar
• Hoffman, P. J. (1960). The paramorphic representation of clinical judgment. Psychological Bulletin, 57(2), 116–131. https://doi.org/10.1037/h0047807
   PubMed Web of Science ®Google Scholar
• Hsee, C. K. (1996). The evaluability hypothesis: An explanation for preference reversals between joint and separate evaluations of alternatives. Organizational Behavior and Human Decision Processes, 67(3), 247–257. https://doi.org/10.1006/obhd.1996.0077
   Web of Science ®Google Scholar
• Kane, P. B., & Broomell, S. B. (2020). Applications of the bias–variance decomposition to human forecasting. Journal of Mathematical Psychology, 98, 102417. https://doi.org/10.1016/j.jmp.2020.102417
   Web of Science ®Google Scholar
• Karelaia, N., & Hogarth, R. M. (2008). Determinants of linear judgment: a meta-analysis of lens model studies. Psychological Bulletin, 134(3), 404–426.
   PubMed Web of Science ®Google Scholar
• Kleinmuntz, D. N., & Schkade, D. A. (1993). Information displays and decision processes. Psychological Science, 4(4), 221–227. https://doi.org/10.1111/j.1467-9280.1993.tb00265.x
   Web of Science ®Google Scholar
• Lewandowsky, S. (2011). Popular consensus: Climate change is set to continue. Psychological Science, 22(4), 460–463. https://doi.org/10.1177/0956797611402515
   PubMed Web of Science ®Google Scholar
• Lewandowsky, S., Risbey, J. S., & Oreskes, N. (2016). The “pause” in global warming: Turning a routine fluctuation into a problem for science. Bulletin of the American Meteorological Society, 97(5), 723–733. https://doi.org/10.1175/BAMS-D-14-00106.1
   Web of Science ®Google Scholar
• Lewis, S. C. (2015). Journalism in an Era of big data: Cases, concepts, and critiques. Digital Journalism, 3(3), 321–330. https://doi.org/10.1080/21670811.2014.976399
   Web of Science ®Google Scholar
• Libby, R. (1976). Man versus model of man: Some conflicting evidence. Organizational Behavior and Human Performance, 16(1), 1–12. https://doi.org/10.1016/0030-5073(76)90002-7
   Web of Science ®Google Scholar
• Lim, J. S., & O'Connor, M. (1995). Judgemental adjustment of initial forecasts: Its effectiveness and biases. Journal of Behavioral Decision Making, 8(3), 149–168. https://doi.org/10.1002/bdm.3960080302
   Web of Science ®Google Scholar
• Manski, C. F. (2018). Communicating uncertainty in policy analysis. In Proceedings of the National Academy of Sciences, 201722389.
   Google Scholar
• Meehl, P. E. (1954). Clinical versus statistical prediction: A theoretical analysis and a review of the evidence. University of Minnesota Press.
   Google Scholar
• Önkal, D., Goodwin, P., Thomson, M., Gönül, S., & Pollock, A. (2009). The relative influence of advice from human experts and statistical methods on forecast adjustments. Journal of Behavioral Decision Making, 22(4), 390–409. https://doi.org/10.1002/bdm.637
   Web of Science ®Google Scholar
• Petropoulos, F., Kourentzes, N., Nikolopoulos, K., & Siemsen, E. (2018). Judgmental selection of forecasting models. Journal of Operations Management, 60(1), 34–46. https://doi.org/10.1016/j.jom.2018.05.005
   Google Scholar
• Promberger, M., & Baron, J. (2006). Do patients trust computers? Journal of Behavioral Decision Making, 19(5), 455–468. https://doi.org/10.1002/bdm.542
   Web of Science ®Google Scholar
• Sarbin, T. R. (1943). A contribution to the study of actuarial and individual methods of prediction. American Journal of Sociology, 48(5), 593–602. https://doi.org/10.1086/219248
   Google Scholar
• Shaffer, V. A., Probst, C. A., Merkle, E. C., Arkes, H. R., & Medow, M. A. (2013). Why do patients derogate physicians who use a computer-based diagnostic support system? Medical Decision Making, 33(1), 108–118. https://doi.org/10.1177/0272989X12453501
   PubMed Web of Science ®Google Scholar
• Shalizi, C. (2017). Advanced data analysis from and elementary point of view. Cambridge University Press. http://www.stat.cmu.edu/∼cshalizi/ADAfaEPoV/ADAfaEPoV.pdf
   Google Scholar
• Subbaswamy, A., & Saria, S. (2020). From development to deployment: dataset shift, causality, and shift-stable models in health AI. Biostatistics (Oxford, England), 21(2), 345–352.
   PubMed Web of Science ®Google Scholar
• Taylor, P. F., & Thomas, M. E. (1982). Short term forecasting: horses for courses. Journal of the Operational Research Society, 33(8), 685–694. https://doi.org/10.1057/jors.1982.157
   Web of Science ®Google Scholar
• Tversky, A., & Kahneman, D. (1971). Belief in the law of small numbers. Psychological Bulletin, 76(2), 105–110. https://doi.org/10.1037/h0031322
   Google Scholar