Boosting people's ability to compare risks using simple, step-by-step online tutorials
What is the boost?
A brief step-by-step online tutorial can teach people how to accurately calculate and compare the event rates of two risks (e.g., case-fatality rates for the flu versus COVID-19) by illustrating how to divide the number of key events (e.g., number of deaths among people infected with disease X) by the total number of events (e.g., total number of people infected with disease X).
Which challenges does the boost tackle?
If people focus on the key event (e.g., number of deaths among people infected with disease X), then a common, but less dangerous risk (e.g., a common, but not very lethal disease) can seem more risky than a less common, but more dangereous risk (e.g., a less common, but more lethal disease). The training decreased the likelihood that people mistakenly focused just on the number of key events (e.g., number of deaths among people infected with disease X) without dividing by the total number of events (e.g., total number of people infected with disease X) and can thus improve people’s ability to correctly identify the more dangereous risk (e.g. which of two diseases has a higher case-fatality risk).
Which competences does the boost foster?
Risk literacy: the competence to understand statistical information about risks. Ameliorating whole number bias errors can not only help people think more accurately about, say, COVID-19 statistics expressed as rational numbers, but also about novel future health crises, or any other context that involves information expressed as rational numbers.
What is the evidence behind it?
1297 participants were randomly assigned to an intervention or control condition. Thompson et al. (2021) showed that participants in the intervention condition, relative to those in the control condition, were more accurate and less likely to explicitly mention whole-number-bias errors in their strategy reports as they solved COVID-19-related math problems. At the time of study (mid-March 2020), the statistics for the US were 22,000 flu deaths (relative to 36,000,000 infections = a case-fatality ratio of 22,000/36,000,000 = 0.00061 = .06%) and, at that point, only 9,318 COVID-19 deaths (relative to 227,743 infections = a case-fatality ratio of 9,318/227,743 = 0.041 = 4.1%).
Thompson, C. A., Taber, J. M., Sidney, P. G., Fitzsimmons, C. J., Mielicki, M. K., Matthews, P. G., Schemmel, E. A., Simonovic, N., Foust, J. L., Aurora, P., Disabato, D. J., Seah, T. H. S., Schiller, L. K., & Coifman, K. G. (2021). Math matters: A novel, brief educational intervention decreases whole number bias when reasoning about COVID-19. Journal of Experimental Psychology: Applied, 27(4), 632–656. https://doi.org/10.1037/xap0000403