Causal explanations, error rates, and human judgment biases missing from the COVID-19 narrative and statistics

Last week we wrote about the importance of causal explanations for differences between countries' COVID-19 death rates, and the need for more random testing. Following on from that we now explain the importance of causal modelling in understanding the results of different types of COVID-19 testing in order to expose what is lacking and what is needed to reduce the uncertainty in classifying an individual as infected with COVID-19.

The full report is here:

Fenton, N., Hitman, G. A., Neil, M., Osman, M., & McLachlan, S. (2020). Causal explanations, error rates, and human judgment biases missing from the COVID-19 narrative and statistics. PsyArXiv Preprints. https://doi.org/10.31234/OSF.IO/P39A4

Previous report:

Fenton, N. E., Osman, M., Neil, M., & McLachlan, S. (2020). Coronavirus: country comparisons are pointless unless we account for these biases in testing. The Conversation, April 2, 2020