Abel, E. L., & Kruger, M. L. (2010). Smile intensity in photographs predicts longevity. Psychological Science, 21(4), 542–544. https://doi.org/10.1177/0956797610363775
Adams, R. C., Challenger, A., Bratton, L., Boivin, J., Bott, L., Powell, G., Williams, A., Chambers, C. D., & Sumner, P. (2019). Claims of causality in health news: A randomised trial. BMC Medicine, 17(1), 1–11.
Adams, R. C., Sumner, P., Vivian-Griffiths, S., Barrington, A., Williams, A., Boivin, J., Chambers, C. D., & Bott, L. (2017). How readers understand causal and correlational expressions used in news headlines. Journal of Experimental Psychology: Applied, 23(1), 1–14.
Ahn, W. K., Kalish, C. W., Medin, D. L., & Gelman, S. A. (1995). The role of covariation versus mechanism information in causal attribution. Cognition, 54(3), 299–352.
Ainsworth, S., & Loizou, A. (2003). The effects of self-explaining when learning with text or diagrams. Cognitive Science, 27(4), 669–681. https://doi.org/10.1207/s15516709cog2706_6
Ainsworth, S. E., & Scheiter, K. (2021). Learning by drawing visual representations: Potential, purposes, and practical implications. Current Directions in Psychological Science, 30(1), 61–67. https://doi.org/10.1177/0963721420979582
Amsel, E., Klaczynski, P. A., Johnston, A., Bench, S., Close, J., Sadler, E., & Walker, R. (2008). A dual-process account of the development of scientific reasoning: The nature and development of metacognitive intercession skills. Cognitive Development, 23(4), 452–471. https://doi.org/10.1016/j.cogdev.2008.09.002
Bao, L., Cai, T., Koenig, K., Fang, K., Han, J., Wang, J., et al. (2009). Learning and scientific reasoning. Science, 323(5914), 586–587.
Baram-Tsabari, A., & Osborne, J. (2015). Bridging science education and science communication research. Journal of Research in Science Teaching, 52(2), 135–144. https://doi.org/10.1002/tea.21202
Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182.
Bensley, D. A., Crowe, D. S., Bernhardt, P., Buckner, C., & Allman, A. L. (2010). Teaching and assessing critical thinking skills for argument analysis in psychology. Teaching of Psychology, 37(2), 91–96. https://doi.org/10.1080/00986281003626656
Berthold, K., & Renkl, A. (2009). Instructional aids to support a conceptual understanding of multiple representations. Journal of Educational Psychology, 101(1), 70–87. https://doi.org/10.1037/a0013247
Berthold, K., & Renkl, A. (2010). How to foster active processing of explanations in instructional communication. Educational Psychology Review, 22(1), 25–40. https://doi.org/10.1007/s10648-010-9124-9
Billman, D., Bornstein, B., & Richards, J. (1992). Effects of expectancy on assessing covariation in data: “Prior belief” versus “meaning.” Organizational Behavior and Human Decision Processes, 53(1), 74–88.
Blalock, H. M., Jr. (1987). Some general goals in teaching statistics. Teaching Sociology, 15(2), 164–172.
Bleske-Rechek, A., Gunseor, M. M., & Maly, J. R. (2018). Does the language fit the evidence? Unwarranted causal language in psychological scientists’ scholarly work. The Behavior Therapist, 41(8), 341–352.
Bleske-Rechek, A., Morrison, K. M., & Heidtke, L. D. (2015). Causal inference from descriptions of experimental and non-experimental research: Public understanding of correlation-versus-causation. Journal of General Psychology, 142(1), 48–70.
Bobek, E., & Tversky, B. (2016). Creating visual explanations improves learning. Cognitive Research: Principles and Implications, 1, 27. https://doi.org/10.1186/s41235-016-0031-6
Bott, L., Bratton, L., Diaconu, B., Adams, R. C., Challenger, A., Boivin, J., Williams, A., & Sumner, P. (2019). Caveats in science-based news stories communicate caution without lowering interest. Journal of Experimental Psychology: Applied, 25(4), 517–542.
Bromme, R., & Goldman, S. R. (2014). The public’s bounded understanding of science. Educational Psychologist, 49(2), 59–69. https://doi.org/10.1080/00461520.2014.921572
Brown, A. W., Brown, M. M. B., & Allison, D. B. (2013). Belief beyond the evidence: Using the proposed effect of breakfast on obesity to show 2 practices that distort scientific evidence. American Journal of Clinical Nutrition, 98, 1298–1308. https://doi.org/10.3945/ajcn.113.064410
Bruner, J. S. (1957). Going beyond the information given. In J. S. Bruner, E. Brunswik, L. Festinger, F. Heider, K. F. Muenzinger, C. E. Osgood, & D. Rapaport (Eds.), Contemporary approaches to cognition (pp. 41–69). Cambridge, MA: Harvard University Press. [Reprinted in Bruner, J. S. (1973), Beyond the information given (pp. 218–238). New York: Norton].
Butler, C. S. (1990). Qualifications in science: Modal meanings in scientific texts. In W. Nash (Ed.), The writing scholar: Studies in academic discourse (pp. 137–170). Sage Publications.
Carnevale, A., Strohl, J., & Smith, N. (2009). Help wanted: Postsecondary education and training required. New Directions for Community Colleges, 146, 21–31. https://doi.org/10.1002/cc.363
Cheng, P. (1997). From covariation to causation: A causal power theory. Psychological Review, 104, 367–405.
Chi, M. T. H. (2000). Self-explaining expository texts: The dual process of generating inferences and repairing mental models. In R. Glaser (Ed.), Advances in instructional psychology: Educational design and cognitive science (pp. 161–238). Erlbaum.
Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13(2), 145–182. https://doi.org/10.1016/0364-0213(89)90002-5
Chi, M. T., De Leeuw, N., Chiu, M. H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18(3), 439–477. https://doi.org/10.1207/s15516709cog1803_3
Corrigan, R., & Denton, P. (1996). Causal understanding as a developmental primitive. Developmental Review, 16, 162–202.
Cousineau, D. (2005). Confidence intervals in within-subject designs: A simpler solution to Loftus and Masson’s method. Tutorials in Quantitative Methods for Psychology, 1, 42–45. https://doi.org/10.20982/tqmp.01.1.p042
Crowell, A., & Schunn, C. (2016). Unpacking the relationship between science education and applied scientific literacy. Research in Science Education, 46(1), 129–140. https://doi.org/10.1007/s11165-015-9462-1
Dor, D. (2003). On newspaper headlines as relevance optimizers. Journal of Pragmatics, 35(5), 695–721.
Durant, J. R. (1993). What is scientific literacy? In J. R. Durant & J. Gregory (Eds.), Science and culture in Europe (pp. 129–137). Science Museum.
Durik, A. M., Britt, M. A., Reynolds, R., & Storey, J. (2008). The effects of hedges in persuasive arguments: A nuanced analysis of language. Journal of Language and Social Psychology, 27(3), 217–234.
Durkin, K., & Rittle-Johnson, B. (2012). The effectiveness of using incorrect examples to support learning about decimal magnitude. Learning and Instruction, 22(3), 206–214. https://doi.org/10.1016/j.learninstruc.2011.11.001
Ecker, U. K., Lewandowsky, S., Chang, E. P., & Pillai, R. (2014a). The effects of subtle misinformation in news headlines. Journal of Experimental Psychology: Applied, 20, 323–335. https://doi.org/10.1037/xap0000028
Ecker, U. K., Swire, B., & Lewandowsky, S. (2014b). Correcting misinformation—A challenge for education and cognitive science. In D. N. Rapp & J. L. G. Braasch (Eds.), Processing inaccurate information: Theoretical and applied perspectives from cognitive science and the educational sciences (pp. 13–38). Cambridge, MA: MIT Press.
Elwert, F. (2013). Graphical causal models. In S. L. Morgan (Ed.), Handbook of causal analysis for social research, Handbooks of Sociology and Social Research. Springer. https://doi.org/10.1007/978-94-007-6094-3_13
Evans, D. (2003a). Hierarchy of evidence: A framework for ranking evidence evaluating healthcare interventions. Journal of Clinical Nursing, 12(1), 77–84. https://doi.org/10.1016/j.learninstruc.2011.11.001
Evans, J. (2003b). In two minds: Dual-process accounts of reasoning. Trends in Cognitive Science, 7(10), 454–469. https://doi.org/10.1016/j.tics.2003.08.012
Evans, J., & Curtis-Holmes, J. (2005). Rapid responding increases belief bias: Evidence for the dual-process theory of reasoning. Thinking & Reasoning, 11(4), 382–389. https://doi.org/10.1080/13546780542000005
Fiorella, L., & Zhang, Q. (2018). Drawing boundary conditions for learning by drawing. Educational Psychology Review, 30(3), 1115–1137. https://doi.org/10.1007/s10648-018-9444-8
Fong, G., Krantz, D., & Nisbett, R. (1986). The effects of statistical training on thinking about everyday problems. Cognitive Psychology, 18(3), 253–292. https://doi.org/10.1016/0010-0285(86)90001-0
Fugelsang, J. A., & Thompson, V. A. (2000). Strategy selection in causal reasoning: When beliefs and covariation collide. Canadian Journal of Experimental Psychology, 54, 13–32.
Fugelsang, J. A., & Thompson, V. A. (2003). A dual-process model of belief and evidence interactions in causal reasoning. Memory & Cognition, 31, 800–815.
Gobert, J., & Clement, J. (1999). Effects of student-generated diagrams versus student-generated summaries on conceptual understanding of causal and dynamic knowledge in plate tectonics. Journal of Research in Science Teaching, 36(1), 39–53.
Green, H. J., & Hood, M. (2013). Significance of epistemological beliefs for teaching and learning psychology: A review. Psychology Learning & Teaching, 12(2), 168–178.
Griffiths, T. L., & Tenenbaum, J. B. (2005). Structure and strength in causal induction. Cognitive Psychology, 51(4), 334–384.
Große, C. S., & Renkl, A. (2007). Finding and fixing errors in worked examples: Can this foster learning outcomes? Learning and Instruction, 17(6), 612–634. https://doi.org/10.1016/j.learninstruc.2007.09.008
Grotzer, T. A., & Shane Tutwiler, M. (2014). Simplifying causal complexity: How interactions between modes of causal induction and information availability lead to heuristic-driven reasoning. Mind, Brain, and Education, 8(3), 97–114.
Haber, N., Smith, E. R., Moscoe, E., Andrews, K., Audy, R., Bell, W., et al. (2018). Causal language and strength of inference in academic and media articles shared in social media (CLAIMS): A systematic review. PLoS ONE, 13(5), e0196346. https://doi.org/10.1371/journal.pone.0196346
Hall, S. S., & Seery, B. L. (2006). Behind the facts: Helping students evaluate media reports of psychological research. Teaching of Psychology, 33(2), 101–104. https://doi.org/10.1207/s15328023top3302_4
Halpern, D. F. (1998). Teaching critical thinking for transfer across domains: Disposition, skills, structure training, and metacognitive monitoring. American Psychologist, 53(4), 449–455. https://doi.org/10.1037/0003-066X.53.4.449
Hammond, E. C., & Horn, D. (1954). The relationship between human smoking habits and death rates: A follow-up study of 187,766 men. Journal of the American Medical Association, 155(15), 1316–1328. https://doi.org/10.1001/jama.1954.03690330020006
Hastie, R. (2015). Causal thinking in judgments. In G. Keren and G. Wu (Eds.), The Wiley Blackwell handbook of judgment and decision making, First Edition (pp. 590–628). Wiley. https://doi.org/10.1002/9781118468333.ch21
Hatfield, J., Faunce, G. J., & Soames Job, R. F. (2006). Avoiding confusion surrounding the phrase “correlation does not imply causation.” Teaching of Psychology, 33(1), 49–51.
Hofer, B. K. (2000). Dimensionality and disciplinary differences in personal epistemology. Contemporary Educational Psychology, 25, 378–405. https://doi.org/10.1006/ceps.1999.1026
Horn, K. (2001). The consequences of citing hedged statements in scientific research articles. BioScience, 51(12), 1086–1093.
Huber, C. R., & Kuncel, N. R. (2015). Does college teach critical thinking? A meta-analysis. Review of Educational Research, 20(10), 1–38. https://doi.org/10.3102/0034654315605917
Huggins-Manley, A. C., Wright, E. A., Depue, K., & Oberheim, S. T. (2021). Unsupported causal inferences in the professional counseling literature base. Journal of Counseling and Development, 99(3), 243–251. https://doi.org/10.1002/jcad.12371
Hyland, K. (1998). Boosting, hedging and the negotiation of academic knowledge. Text & Talk, 18(3), 349–382.
Jenkins, E. W. (1994). Scientific literacy. In T. Husen & T. N. Postlethwaite (Eds.), The international encyclopedia of education (2nd ed., Vol. 9, pp. 5345–5350). Pergamon Press.
Jensen, J. D. (2008). Scientific uncertainty in news coverage of cancer research: Effects of hedging on scientists’ and journalists’ credibility. Human Communication Research, 34, 347–369.
Johnson, H. M., & Seifert, C. M. (1994). Sources of the continued influence effect: When discredited information in memory affects later inferences. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20(6), 1420–1436.
Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus & Giroux.
Kida, T. E. (2006). Don’t believe everything you think: The 6 basic mistakes we make in thinking. Prometheus Books.
Klaczynski, P. A. (2000). Motivated scientific reasoning biases, epistemological beliefs, and theory polarization: A two-process approach to adolescent cognition. Child Development, 71(5), 1347–1366. https://doi.org/10.1111/1467-8624.00232
Koch, C., & Wüstemann, J. (2014). Experimental analysis. In The Oxford handbook of public accountability (pp. 127–142).
Koehler, J. J. (1993). The influence of prior beliefs on scientific judgments of evidence quality. Organizational Behavior and Human Decision Processes, 56, 28–55.
Kolstø, S. D., Bungum, B., Arnesen, E., Isnes, A., Kristensen, T., Mathiassen, K., & Ulvik, M. (2006). Science students’ critical examination of scientific information related to socio-scientific issues. Science Education, 90(4), 632–655. https://doi.org/10.1002/sce.20133
Kosonen, P., & Winne, P. H. (1995). Effects of teaching statistical laws on reasoning about everyday problems. Journal of Educational Psychology, 87(1), 33. https://doi.org/10.1037/0022-0622.214.171.124
Kuhn, D. (1993). Connecting scientific and informal reasoning. Merrill-Palmer Quarterly, 39(1), 74–103.
Kuhn, D. (2005). Education for thinking. Harvard University Press.
Kuhn, D. (2012). The development of causal reasoning. Wires Cognitive Science, 3, 327–335. https://doi.org/10.1002/wcs.1160
Kuhn, D., Amsel, E., O’Loughlin, M., Schauble, L., Leadbeater, B., & Yotive, W. (1988). Developmental psychology series. The development of scientific thinking skills. Academic Press.
Kuhn, D., & Dean, D., Jr. (2004). Connecting scientific reasoning and causal inference. Journal of Cognition and Development, 5(2), 261–288.
Kuhn, D., Garcia-Mila, M., Zohar, A., & Andersen, C. (1995). Strategies of knowledge acquisition. Monographs of the Society for Research in Child Development, 60, i 157.
Kuhn, D., Iordanou, K., Pease, M., & Wirkala, C. (2008). Beyond control of variables: What needs to develop to achieve skilled scientific thinking? Cognitive Development, 23, 435–451.
Kunda, Z. (1990). The case for motivated reasoning. Psychological Bulletin, 108(3), 480–498. https://doi.org/10.1037/0033-2909.108.3.480
Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159–174.
Lazarus, C., Haneef, R., Ravaud, P., & Boutron, I. (2015). Classification and prevalence of spin in abstracts of non-randomized studies evaluating an intervention. BMC Medical Research Methodology, 15, 85. https://doi.org/10.1186/s12874-015-0079-x
Lee, L. O., James, P., Zevon, E. S., Kim, E. S., Trudel-Fitzgerald, C., Spiro, A., III., Grodstein, F., & Kubzansky, L. D. (2019). Optimism is associated with exceptional longevity in 2 epidemiologic cohorts of men and women. Proceedings of the National Academy of Sciences, 116(37), 18357–18362. https://doi.org/10.1073/pnas.1900712116
Lehrer, R., & Schauble, L. (2006). Scientific thinking and science literacy. In W. Damon, & R. Lerner (Series Eds.) & K. A. Renninger, & I. E. Sigel (Vol. Eds.), Handbook of child psychology Vol. 4: Child psychology in practice (6th ed.). New York: Wiley. https://doi.org/10.1002/9780470147658.chpsy0405.
Lewandowsky, S., Ecker, U. K., Seifert, C. M., Schwarz, N., & Cook, J. (2012). Misinformation and its correction: Continued influence and successful debiasing. Psychological Science in the Public Interest, 13(3), 106–131. https://doi.org/10.1177/1529100612451018
Marinescu, I. E., Lawlor, P. N., & Kording, K. P. (2018). Quasi-experimental causality in neuroscience and behavioural research. Nature Human Behaviour, 2(12), 891–898.
Mayer, R. E. (2005). Cognitive theory of multimedia learning. The Cambridge handbook of multimedia learning, 41, 31–48.
Mayer, R. E. (2020). Multimedia learning. Cambridge University Press.
Michal, A. L., Zhong, Y., & Shah, P. (2021). When and why do people act on flawed science? Effects of anecdotes and prior beliefs on evidence-based decision-making. Cognitive Research: Principles and Implications, 6, 28. https://doi.org/10.1186/s41235-021-00293-2
Miller, J. D. (1996). Scientific literacy for effective citizenship: Science/technology/society as reform in science education. SUNY Press.
Morling, B. (2014). Research methods in psychology: Evaluating a world of information. W.W. Norton and Company
Mueller, J. (2020). Correlation or causation? Retrieved June 1, 2021, from http://jfmueller.faculty.noctrl.edu/100/correlation_or_causation.htm
Mueller, J. F., & Coon, H. M. (2013). Undergraduates’ ability to recognize correlational and causal language before and after explicit instruction. Teaching of Psychology, 40(4), 288–293. https://doi.org/10.1177/0098628313501038
Next Generation Science Standards Lead States. (2013). Next generation science standards: For states, by states. The National Academies Press.
Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2(2), 175–220. https://doi.org/10.1037/1089-26126.96.36.199
Norcross, J. C., Gerrity, D. M., & Hogan, E. M. (1993). Some outcomes and lessons from a cross-sectional evaluation of psychology undergraduates. Teaching of Psychology, 20(2), 93–96. https://doi.org/10.1207/s15328023top2002_6
Norris, S. P., & Phillips, L. M. (1994). Interpreting pragmatic meaning when reading popular reports of science. Journal of Research in Science Teaching, 31(9), 947–967. https://doi.org/10.1002/tea.3660310909
Norris, S. P., Phillips, L. M., & Korpan, C. A. (2003). University students’ interpretation of media reports of science and its relationship to background knowledge, interest, and reading difficulty. Public Understanding of Science, 12(2), 123–145. https://doi.org/10.1177/09636625030122001
NTSA Framework (2012). Retrieved June 1, 2021 from https://ngss.nsta.org/practices.aspx?id=7
Ohlsson, S. (1996). Learning from error and the design of task environments. International Journal of Educational Research, 25(5), 419–448.
Pearl, J. (1995). Causal diagrams for empirical research. Biometrika, 82(4), 669–688.
Pearl, J. (2000). Causality: Models, reasoning, and inference. Cambridge University Press.
Pearl, J., & Mackenzie, D. (2018). The book of why: The new science of cause and effect. Basic Books.
Picardi, C. A., & Masick, K. D. (2013). Research methods: Designing and conducting research with a real-world focus. SAGE Publications.
Pressley, M., Wood, E., Woloshyn, V. E., Martin, V., King, A., & Menke, D. (1992). Encouraging mindful use of prior knowledge: Attempting to construct explanatory answers facilitates learning. Educational Psychologist, 27(1), 91–109.
Reinhart, A. L., Haring, S. H., Levin, J. R., Patall, E. A., & Robinson, D. H. (2013). Models of not-so-good behavior: Yet another way to squeeze causality and recommendations for practice out of correlational data. Journal of Educational Psychology, 105, 241–247.
Reis, H. T., & Judd, C. M. (2000). Handbook of research methods in social and personality psychology. Cambridge University Press.
Renken, M. D., McMahan, E. A., & Nitkova, M. (2015). Initial validation of an instrument measuring psychology-specific epistemological beliefs. Teaching of Psychology, 42(2), 126–136.
Renkl, A., Stark, R., Gruber, H., & Mandl, H. (1998). Learning from worked-out examples: The effects of example variability and elicited self-explanations. Contemporary Educational Psychology, 23(1), 90–108. https://doi.org/10.1006/ceps.1997.0959
Rhodes, R. E., Rodriguez, F., & Shah, P. (2014). Explaining the alluring influence of neuroscience information on scientific reasoning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 40(5), 1432–1440. https://doi.org/10.1037/a0036844
Rodriguez, F., Ng, A., & Shah, P. (2016a). Do college students notice errors in evidence when critically evaluating research findings? Journal on Excellence in College Teaching, 27(3), 63–78.
Rodriguez, F., Rhodes, R. E., Miller, K., & Shah, P. (2016b). Examining the influence of anecdotal stories and the interplay of individual differences on reasoning. Thinking & Reasoning, 22(3), 274–296. https://doi.org/10.1080/13546783.2016.1139506
Ryder, J. (2001). Identifying science understanding for functional scientific literacy. Studies in Science Education, 36(1), 1–44. https://doi.org/10.1080/03057260108560166
Sá, W. C., West, R. F., & Stanovich, K. E. (1999). The domain specificity and generality of belief bias: Searching for a generalizable critical thinking skill. Journal of Educational Psychology, 91(3), 497–510. https://doi.org/10.1037/0022-06188.8.131.527
Schellenberg, E. G. (2020). Correlation = causation? Music training, psychology, and neuroscience. Psychology of Aesthetics, Creativity, and the Arts, 14(4), 475–480.
Seifert, C. M., & Hutchins, E. L. (1992). Error as opportunity: Learning in a cooperative task. Human-Computer Interaction, 7(4), 409–435.
Shafto, P., Goodman, N. D., & Griffiths, T. L. (2014). A rational account of pedagogical reasoning: Teaching by, and learning from, examples. Cognitive Psychology, 71(1), 55–89. https://doi.org/10.1016/j.cogpsych.2013.12.004
Shah, P., Michal, A., Ibrahim, A., Rhodes, R., & Rodriguez, F. (2017). What makes everyday scientific reasoning so challenging? The Psychology of Learning and Motivation, 66, 251–299. https://doi.org/10.1016/bs.plm.2016.11.006
Shou, Y., & Smithson, M. (2015). Effects of question formats on causal judgments and model evaluation. Frontiers in Psychology, 6, Article 467. https://doi.org/10.3389/fpsyg.2015.00467.
Siegler, R. S., & Chen, Z. (2008). Differentiation and integration: Guiding principles for analyzing cognitive change. Developmental Science, 11(4), 433–448. https://doi.org/10.1111/j.1467-7687.2008.00689.x
Sinatra, G. M., Kienhues, D., & Hofer, B. (2014). Addressing challenges to public understanding of science: Epistemic cognition, motivated reasoning, and conceptual change. Educational Psychologist, 49(2), 123–138. https://doi.org/10.1080/00461520.2014.916216
Skelton, J. (1988). The care and maintenance of hedges. ELT Journal, 42(1), 37–43.
Sloman, S. A. (2005). Causal models. Oxford University Press.
Sloman, S. A., & Lagnado, D. A. (2003). Causal invariance in reasoning and learning. The Psychology of Learning and Motivation, 44, 287–325.
Sloman, S., & Lagnado, D. A. (2015). Causality in thought. Annual Review of Psychology, 66, 223–247.
Stadtler, M., Scharrer, L., Brummernhenrich, B., & Bromme, R. (2013). Dealing with uncertainty: Readers’ memory for and use of conflicting information from science texts as function of presentation format and source expertise. Cognition and Instruction, 31(2), 130–150. https://doi.org/10.1080/07370008.2013.769996
Stanovich, K. E. (2009). What intelligence tests miss: The psychology of rational thought. Yale University.
Stanovich, K. E. (2010). How to think straight about psychology (9th ed.). Allyn & Bacon.
Stark, R., Kopp, V., & Fischer, M. R. (2011). Case-based learning with worked examples in complex domains: Two experimental studies in undergraduate medical education. Learning and Instruction, 21(1), 22–33. https://doi.org/10.1016/j.learninstruc.2009.10.001
Stark, R., Mandl, H., Gruber, H., & Renkl, A. (2002). Conditions and effects of example elaboration. Learning and Instruction, 12(1), 39–60. https://doi.org/10.1016/s0959-4752(01)00015-9
Steffens, B., Britt, M. A., Braasch, J. L., Strømsø, H., & Bråten, I. (2014). Memory for scientific arguments and their sources: Claim–evidence consistency matters. Discourse Processes, 51, 117–142.
Sullivan, G. M. (2011). Getting off the “gold standard”: Randomized controlled trials and education research. Journal of Graduate Medical Education, 3(3), 285–289. https://doi.org/10.4300/JGME-D-11-00147.1
Sumner, P., Vivian-Griffiths, S., Boivin, J., Williams, A., Venetis, C. A., Davies, A., Ogden, J., Whelan, L., Hughes, B., Dalton, B., Boy, F., & Chambers, C. D. (2014). The association between exaggeration in health related science news and academic press releases: Retrospective observational study. British Medical Journal, 2014(349), g7015. https://doi.org/10.1136/bmj.g7015
Tal, A., & Wansink, B. (2016). Blinded with science: Trivial graphs and formulas increase ad persuasiveness and belief in product efficacy. Public Understanding of Science, 25(1), 117–125. https://doi.org/10.1177/0963662514549688
Topor, D. D. (2019). If you’re happy and you know it… you may live longer. Harvard Health Blog, Harvard Medical School (10.16.2019). Retrieved June 1, 2021, from https://www.health.harvard.edu/blog/if-you-are-happy-and-you-know-it-you-may-live-longer-2019101618020
Trefil, J. (2008). Science education for everyone: Why and what? Liberal Education, 94(2), 6–11. Retrieved 6/15/2021 from: https://www.aacu.org/publications-research/periodicals/science-education-everyone-why-and-what
Tversky, A., & Kahneman, D. (1977). Causal thinking in judgment under uncertainty. In J. Hintikka & R. E. Butts (Eds.), Basic problems in methodology and linguistics (pp. 167–190). Springer.
Van Gog, T., & Rummel, N. (2010). Example-based learning: Integrating cognitive and social-cognitive research perspectives. Educational Psychology Review, 22(2), 155–174. https://doi.org/10.1007/s10648-010-9134-7
Waldmann, M. R., Hagmayer, Y., & Blaisdell, A. P. (2006). Beyond the information given: Causal models in learning and reasoning. Current Directions in Psychological Science, 15(6), 307–311. https://doi.org/10.1111/j.1467-8721.2006.00458.x
Whoriskey, P. (2011). Requiring algebra 2 in high school gains momentum. The Washington Post. Retrieved 6/15/2021 from https://www.washingtonpost.com/business/economy/requiring_algebra_ii_in_high_school_gains_momentum_nationwide/2011/04/01/AF7FBWXC_story.html?noredirect=on&utm_term=.a153d444a4bd
Wright, J. C., & Murphy, G. L. (1984). The utility of theories in intuitive statistics: The robustness of theory-based judgments. Journal of Experimental Psychology: General, 113, 301–322.
Xiong, C., Shapiro, J., Hullman, J., & Franconeri, S. (2020). Illusion of causality in visualized data. IEEE Transactions on Visualization and Computer Graphics, 26(1), 853–862. https://doi.org/10.1109/TVCG.2019.2934399
Yavchitz, A., Boutron, I., Bafeta, A., Marroun, I., Charles, P., Mantz, J., & Ravaud, P. (2012). Misrepresentation of randomized controlled trials in press releases and news coverage: A cohort study. PLoS Medicine, 9, e1001308.
Zimmerman, C., Bisanz, G. L., Bisanz, J., Klein, J. S., & Klein, P. (2001). Science at the supermarket: A comparison of what appears in the popular press, experts’ advice to readers, and what students want to know. Public Understanding of Science, 10(1), 37–58.
Zweig, M., & Devoto, E. (2015). Observational studies—Does the language fit the evidence? Association versus causation. Health News Review. Retrieved 6/15/2021 from https://www.healthnewsreview.org/toolkit/tips-for-understanding-studies/does-the-language-fit-the-evidence-association-versus-causation/