Click below to find out how risk literate you are compared to educated people from around the world.
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Risk is everywhere and many people, including doctors, financial consultants, judges and journalists struggle to accurately perceive, evaluate, and communicate risks.
Our software will give you automatic, personalized feedback indicating your score on the Berlin Numeracy Test, a validated research tool used to predict risk literacy in educated people from around the world. You will not be required to enter any identifying personal information (e.g., participation is anonymous, there are no requests for emails or names) although we may ask some basic demographic or validation questions so that we can continue to improve the test.
Risk literacy refers to one’s practical ability to evaluate and understand risk in the service of skilled and informed decision making. In some ways our ability to understand risk depends on external factors like the design of risk communications (e.g., simple visual aids can promote or bias risk comprehension). In other ways risk literacy depends on specific skills and abilities. For example, one’s practical understanding of mathematics (i.e., numeracy especially statistical numeracy) tends to be the strongest single predictor of risk literacy and general decision making skill (for a recent review see Skilled Decision Theory; Cokely et al., 2018). In part this is because math is the language of risk and science. The statistical aspects of numeracy are essential features of risk assessment in health, business, and engineering. Statistical information plays some role in virtually all formal risk analyses and risk communications. These statistical numeracy skills are also among the most influential educational factors associated with economic prosperity in industrialized countries. Nevertheless, the psychology of skilled decision making isn’t about cold calculation or simply “doing the math”. Skilled decision making generally involves identification and integration of complex trade-offs, risks, rewards, etc., in the context of deeply held personal values and responsibilities. In addition to mathematical competency, skilled and informed decision making rely on (1) heuristic deliberation and representative understanding (e.g., Skilled Decision Theory; Cokely et al., 2012; 2018; Gigerenzer et al., 1999; 2012); (2) integrated emotional and analytic thinking (e.g., precise affect; Peters et al., 2006); and (3) meaningful intuition (e.g., fuzzy-gist representation; Reyna et al., 2009). Risk literacy promotes skilled and informed decision making by helping people understand and evaluate risk and reward.
In July of 1654 Blaise Pascal began corresponding with Pierre de Fermat about the division of stakes in a game of chance (i.e., gambling), and how best to formally interpret risk and reward in this context (i.e., “Problem of Points”). Although this was not the first attempt to systematize chance, these exchanges became the founding documents of mathematical probability theory, which is at the heart of modern science and risk analysis.
The word “risk” has many uses (e.g., exposure to danger and loss; variability in probability distributions; the effect of uncertainty on objectives). Since 1921 the psychology of economic decision making and risk has been formally connected to probabilistic and quantitative thinking.
“The great body of physical science, a great deal of the essential fact of financial science, and endless social and political problems are only accessible and only thinkable to those who have had a sound training in mathematical analysis, and the time may not be very remote when it will be understood that for complete initiation as an efficient citizen of one of the great complex world-wide States that are now developing, it is as necessary to be able to compute, to think in averages and maxima and minima, as it is now to be able to read and write.” - Mankind in the Making, H.G. Wells (p. 204, 1903)
Mission: Advance and promote the Science for Informed Decision Making with inclusive outreach and user-friendly technology and education. RiskLiteracy.org is a non-profit university-based international science for society virtual collaboration. Our website features the Berlin Numeracy Test offering personalized feedback about one’s ability to understand and respond to risks compared to educated people from around the world. Initially developed and validated at the Max Planck Institute for Human Development, the Berlin Numeracy Test is one of the world’s most efficient predictors of skilled decision making and risk literacy. The Berlin Numeracy Test was created to help increase public awareness and to improve research conducted with diverse cultures and samples (e.g., computer literate adults; educated people from the US, Europe, and Asia; highly educated medical, legal, and financial professionals). Because some test formats are internet-based and available in multiple languages data can be collected and analyzed by nearly any internet-ready device (e.g., smart phones; computers), providing immediate and personalized feedback. RiskLiteracy.org can be used by individuals who want to learn more about their abilities and limitations or it can be used by researchers who want to learn about, connect with, and contribute to informed decision making science and outreach.
About This Website: RiskLiteracy.org is a nonprofit university-based project designed to help increase awareness about risk literacy (i.e., the ability to understand, evaluate, and make good decisions about risk). The site features the Berlin Numeracy Test and other interactive content, offering automated scoring and personalized feedback and training. Since 2012, 50,000+ people from 156 countries have taken the Berlin Numeracy Test at RiskLiteracy.org.
Project Management and Development: Riskliteracy.org functions as a virtual collaborative research network and outreach initiative managed by a Board of Directors (i.e., Dr. Edward Cokely, Dr. Adam Feltz, and Dr. Rocio Garcia-Retamero) in consultation with a Scientific Advisory Board (e.g., Dr. Mirta Galesic, Dr. Gerd Gigerenzer, Dr. Ellen Peters, Dr. Hank Jenkins-Smith, Dr. Wandi Bruine de Bruin, Dr. Rob Hamm), associate scientists, and community partners. The RiskLiteracy.org project was initiated at the Max Planck Institute for Human Development in 2007 under the direction of Dr. Edward Cokely, Dr. Mirta Galesic, Dr. Adam Feltz, and Dr. Rocio Garcia-Retamero, with support from Dr. Gerd Gigerenzer and the Center for Adaptive Behavior and Cognition. Website management and daily technical operations are provided by members of the Decision Psychology Lab and the National Institute for Risk & Resilience on the Research Campus at the University of Oklahoma. For questions, connections, and a list of affiliated researchers please see our People page. If you have ideas about how you can contribute to the project we encourage you to contact us.
Risk Literacy Training: We are currently developing several interactive tutorials to help people explore and avoid some common risky decision making errors. As soon as tutorials are available we will add them to this page. Please check back every 3-6 months for new tutorials.
Use the Berlin Numeracy Test
The Berlin Numeracy Tests are fast user-friendly psychometric assessment technologies (e.g., measurement instruments), validated for use with educated samples from diverse countries and cultures (e.g., college students, computer-literate adults, physicians). The simplest Berlin Numeracy Test is a traditional 4 question paper and pencil test that takes < 4 minutes to complete. The computer adaptive version of the Berlin Numeracy Test takes about 2 minutes to complete because it only requires 2-3 questions that are selected based on participant performance. If a test-taker answers the first question right or wrong then a harder or easier question is automatically presented. We have also validated multiple choice formats, parallel forms, extensive full-scale and sub-scale tests (e.g., numeracy for certainty v. uncertainty), as well as very fast single-item tests for use with general community or highly-educated samples (i.e., median-split). All test formats are designed to address psychometric limitations of other numeracy and skilled decision making tests (e.g., negative skew, construct validity). A growing body of research indicates that the Berlin Numeracy Test tends to be the most efficient stand-alone assessment of numeracy, risk literacy, and general decision making skill currently available, more than doubling the predictive power of much longer numeracy and cognitive ability tests (e.g., intelligence, cognitive reflection, working memory; Cokely et al., 2012). We’ve also validated simple systems to combine our tests with other instruments for more extensive analyses, which can be valuable when working sub-samples like less-numerate patient groups. To select the best test format for your needs please see the different versions of the BNT available If you are unsure what version you should use, please use our Test Recommendation Tool
If you would like help building the Computer Adaptive Berlin Numeracy Test, please contact us. We can provide a Qualtrics link to an individualized survey.
Cokely, E.T., Galesic, M., Schulz, E., Ghazal, S., & Garcia-Retamero, R. (2012). Measuring risk literacy: The Berlin Numeracy Test. Judgment and Decision Making, 7, 25-47. Available Here
Description: A 1-4 item (ca. 3 minute) test of statistical numeracy and risk literacy that is well-suited for use with moderate-to-highly numerate individuals from diverse industrialized countries (e.g., college students, computer literate adults, physicians). Scores from the 3 item Schwartz et al. (1997) test can be added to the Berlin Numeracy Test for a 5 minute assessment that provides additional discriminability at lower levels of numeracy (i.e., the BNT-S).
In 21 studies (n=5336) we found robust psychometric discriminability across 15 countries (e.g., Germany, Pakistan, Japan, USA) and diverse samples (e.g., medical professionals, general populations, Mechanical Turk). Analyses demonstrated desirable patterns of convergent validity (e.g., cognitive ability), discriminant validity (e.g., personality, motivation), and criterion validity (e.g., numerical and non-numerical questions about risk). The Berlin Numeracy Test was found to be the strongest predictor of comprehension of everyday risks (e.g., evaluating claims about products and treatments; interpreting forecasts), doubling the predictive power of other numeracy instruments and accounting for unique variance beyond other cognitive tests (e.g., cognitive reflection, working memory, intelligence).
Vincent Ybarra, MS, ABD - Research Interests: Confidence, Intelligent Tutors, Energy Risk Literacy, Decision Making
Madhuri Ramasubramanian, MS - Research Interests: Risk Perception, Risk Communication, Decision Making Profiles
Dr. Wandi Bruine de Bruin, Ph.D. Provost Professor of Public Policy, Psychology, and Behavioral Science, University of Southern California
Dr. Mirta Galesic, Ph.D. Professor and Cowan Chair of Social Dynamics, Santa Fe Institute
Dr. Gerd Gigerenzer, Ph.D. Director, Harding Center for Risk Literacy, Max Planck Institute for Human Development
Dr. Robert M. Hamm, Ph.D. Professor of Family and Preventive Medicine Director, Clinical Decision Making Program, University of Oklahoma Health Sciences Center
Dr. Hank Jenkins-Smith, Ph.D. George Lynn Cross Research Professor of Political Science Director, National Institute for Risk & Resilience, University of Oklahoma
Dr. Ellen Peters, Ph.D. Philip H. Knight Chair, Director, Center for Science Communication Research (SCR), University of Oregon
Dr. Katrina Ellis, Ph.D. Florida Institute of Technology, Lead Affiliate for RiskLiteracy Florida
Dr. Saima Ghazal, Ph.D. University of the Punjab, Lead Affiliate for RiskLiteracy Pakistan & India
Dr. Ulrich Hoffrage, Ph.D. University of Lausanne, Lead Affiliate for RiskLiteracy Switzerland
Dr. Jonathan Huck, Ph.D. WGU Labs
Dr. Niklas Keller, Ph.D. Charité Hospital
Dr. Astrid Kause, Ph.D. Centre for Decision Research, University of Leeds, Max Planck Institute for Human Development
Dr. Takashi Kusumi, Ph.D. Kyoto University, Lead Affiliate for RiskLiteracy Japan
Dr. Marcus Lindskog, Ph.D. Uppsala University, Lead Affiliate for RiskLiteracy Sweden
Ms. Brittany Nelson Michigan Techological University
Dr. Yasmina Okan, Ph.D. Leeds Business School, Lead Affiliate for RiskLiteracy England
Dr. Dafina Petrova, Ph.D. University of Granada, Lead Affiliate for RiskLiteracy Romania, Spain, and the Netherlands
Dr. Erich Petushek, Ph.D. Michigan Techological University, Lead Affiliate for RiskLiteracy Norway
Dr. Paul Price, Ph.D. California State University, Fresno, Lead Affiliate for RiskLiteracy California
Dr. Michael Prietula, Ph.D. Emory University, Lead Affiliate for RiskLiteracy Georgia
Dr. Jing Qian, Ph.D. Tsinghua University, Lead Affiliate for RiskLiteracy China
Dr. Nenad Radakovic, Ph.D. College of Charleston, Lead Affiliate for RiskLiteracy South Carolina
Dr. Matthew Rhodes, Ph.D. Colorado State University, Lead Affiliate for RiskLiteracy Colorado
Dr. Andrew Smith, Ph.D. Appalachian State University, Lead Affiliate for RiskLiteracy North Carolina
Dr. Joel Suss, Ph.D. Wichita State University, Lead Affiliate for RiskLiteracy Kansas
Dr. Barnabás Szászi, Ph.D. ELTE University, Lead Affiliate for RiskLiteracy Hungary
Dr. Helena Szrek, Ph.D. University of Porto, Lead Affiliate for RiskLiteracy Portugal & South Africa
Dr. Brian Taylor, Ph.D. Ulster University, Lead Affiliate RiskLiteracy Ireland
Dr. Jakub Traczyk, Ph.D. SWPS University of Social Sciences and Humanities, Lead Affiliate for RiskLiteracy Poland
Dr. Carissa A. Zimmerman, Ph.D. Rice University, Lead Affiliate for RiskLiteracy Texas
Test Validation References:
Cokely, E.T., Ghazal, S., Galesic, M., Garcia-Retamero, R., & Schulz, E. (2013). How to measure risk comprehension in educated samples. In R. Garcia-Retamero & M. Galesic (Ed.), Transparent communication of risks about health: Overcoming cultural differences (pp. 29-52). New York: Springer.
Cokely, E.T., Ghazal, S., & Garcia-Retamero, R. (2014). Measuring numeracy. In B. L. Anderson & J. Schulkin (Eds.), Numerical Reasoning in Judgments and Decision Making about Health. Cambridge University Press, Cambridge, UK.
Garcia-Retamero, R., & Cokely, E.T. (2013). Communicating health risks with visual aids. Current Directions in Psychological Science, 22, 392-399.
Garcia-Retamero, R., Wicki, B., Cokely, E.T., & Hanson, B. (2014). Factors predicting surgeons’ preferred and actual roles in interactions with their patients. Health Psychology.
Ghazal, S., Cokely, E.T., & Garcia-Retamero (2014). Predicting biases in very highly educated samples: Numeracy and metacognition. Judgment and Decision Making, 9, 15-34.
Find papers citing the Berlin Numeracy Test at Google Scholar Citations
Appelt, K. C., Milch, K. F., Handgraaf, M. J. J., & Weber, E. U. (2011). The Decision Making Individual Differences Inventory and guidelines for the study of individual differences in judgment and decision-making research. Judgment and Decision Making, 6, 252-262. Description: An online database of psychometric instruments and inventories used in the decision sciences. http://www.sjdm.org/dmidi
Blais, A. R., & Weber, E. U. (2006). A Domain-Specific Risk-Taking (DOSPERT) scale for adult populations. Judgment and Decision Making, 1(1), 33-47. Description: The updated, brief version of DOSPERT was designed to more quickly assess risk preferences through self-report in five domains.
Cokely, E.T., & Kelley, C.M. (2009). Cognitive abilities and superior decision making under risk: A protocol analysis and process model evaluation. Judgment and Decision Making, 4, 20-33. http://journal.sjdm.org/81125/jdm81125.pdf
Fagerlin, A., Zikmund-Fisher, B., Ubel, P., Jankovic, A., Derry, H., & Smith, D. (2007). Measuring numeracy without a math test: Development of the subjective numeracy scale. Medical Decision Making, 27, 672–680. Description: A short instrument for collecting subjective numeracy self-estimates that is well suited for differentiating among low-to-moderately numerate individuals.
Galesic, M., y García-Retamero, R. (2011b). Graph literacy: A cross-cultural comparison. Medical Decision Making, 31, 444-457. Description: A brief test that measures individual differences in Graph Literacy.
Gigerenzer, G. (2002). Calculated risks: How to know when numbers deceive you. New York: Simon & Schuster.
Gigerenzer, G. (2012). Risk literacy. In J. Brockman (Ed.), This will make you smarter: New scientific concepts to improve your thinking (pp. 259-261). New York: Harper Perennial.
Gigerenzer, G., Gaissmaier, W., Kurz-Milcke, E., Schwartz, L. M., & Woloshin, S. (2007). Helping doctors and patients to make sense of health statistics. Psychological Science in the Public Interest, 8, 53–96.
Kutner, M., Greenberg, E., Jin, Y., Boyle, B., Hsu, Y., & Dunleavy, E. (2007). Literacy in everyday life: Results from the 2003 National Assessment of Adult Literacy (NAAL). National Center for Education Statistics. Institute of Education Sciences. Retrieved from http://nces.ed.gov/naal
Lindskog, M., Kerimi, N., Winman A. & Juslin, P. (2015). A Swedish validation of the Berlin Numeracy Test. Scandinavian Journal of Psychology.
Lipkus, I. M., & Peters, E. (2009). Understanding the role of numeracy in health: Proposed theoretical framework and practical insights. Health Education and Behavior, 36(6), 1065-1081.
Lipkus, I. M., Samsa, G., & Rimer, B. K. (2001). General performance on a numeracy scale among highly-educated samples. Medical Decision Making, 21, 37-44. Description: 11 item test (5-10 minutes) that measures statistical numeracy and is suited for use with low-to-moderately numerate individuals.
Peters, E. (2012). Beyond comprehension: The role of numeracy in judgments and decisions. Current Directions in Psychological Science, 21(1), 31-35.
Peters, E., Västfjäll, D., Slovic, P., Mertz, C. K., Mazzocco, K., & Dickert, S. (2006). Numeracy and decision making. Psychological Science, 17(5), 407-413.
Reyna, V. F., Nelson, W. L., Han, P. K., & Dieckmann, N. F. (2009). How numeracy influences risk comprehension and medical decision making. Psychological Bulletin, 135, 943-973.
Shapira, M. M., Walker C. M., Cappaert, K. J., Ganschow, P. S., Fletcher, K. E., McGinley, E. L., Del Pozo, S., … Jacobs, E. A. (2012). The Numeracy Understand in Medicine Instrument (NUMi): A measure of health numeracy developed using Item Response Theory. Medical Decision Making, 32, 851-865.
Schwartz, L. M. L., Woloshin, S. S., Black, W. C. W., & Welch, H. G. H. (1997). The role of numeracy in understanding the benefit of screening mammography. Annals of Internal Medicine, 127, 966-972. Description: 3 item test (1-2 minutes) test that is well suited for use with low-to-moderately numerate individuals.
Weber, E. U., Blais, A., & Betz, N. E. (2002). A domain-specific risk-attitude scale: Measuring risk perceptions and risk behaviors. Journal of Behavioral Decision Making, 15(4), 263-290. Description: The DOSPERT was designed to assess risk preferences through self-report in five domains.
Weller, J., Dieckmann, N. F., Tusler, M., Mertz, C. K., Burns, W., & Peters, E. (2013). Development and testing of an abbreviated numeracy scale: A Rasch Analysis approach. Journal of Behavioral Decision Making, 26(2),198-212. Description: A Rasch test (est. 10 minutes) that combines items from various tests (e.g., cognitive reflection test, Lipkus et al. 2011) and is designed for use with the general US population. NOTE: One item on the scale was updated in 2013.
We thank the members of the Center for Adaptive Behavior and Cognition and of the Harding Center for Risk Literacy at the Max Planck Institute for Human Development, particularly Dr. Gerd Gigerenzer and Dr. Lael Schooler for crucial initial and ongoing support. We offer special thanks to Dr. Kelvin Droegemeier and to members of the National Institute for Risk and Resilience at OU for their vision and commitment to transformative science that empowers and changes lives. We offer special thanks to all our members, participants, and collaborators, and to the esteemed members of our scientific advisory board, for their continuing support, guidance, and critical-constructive feedback.
Grants and Financial Support We are grateful for generous support from:
We are indebted to the following researchers and the supporting institutions for cross-cultural and other data collection/analysis: (alphabetical order)