“Human cognition is characterized by cognitive biases, which systematically lead to errors in judgment: errors that can potentially be catastrophic (e.g., overconfidence as a cause of war). For example, a strong case can be made that Russia’s invasion of Ukraine has been an irrational decision of Putin, a consequence of which is potential nuclear war. Overconfidence is a cause of wars and of underpreparation for catastrophes (e.g., pandemics, as illustrated by the COVID-19 pandemic).
One way to reduce detrimental and potentially catastrophic decisions is to provide people with statistical training that can help empower beneficial decision-making via correct calibration of beliefs. (Statistical training to keep track of the mean past payoff/observation can be helpful in a general sense; see my paper on the evolution of human cognitive biases and implications.) At the moment, statistical training is provided to a very small percentage of people, and most provisions of statistical training are not laser-focused on the improvement of practical learning/decision-making capabilities, but for other indirect goals (e.g., prerequisite for STEM undergraduate majors). It may be helpful to (1) encourage practical, impactful aspects in the provision of statistical training and (2) broaden its provision to a wider segment of people.”
(Quote from my post ’Broadening statistical education”)
Given resource limitations, it may make sense to target the provision of practical statistics training to high-impact decision-makers, such as those in government. An ambitious example is that just as the US president is given a confidential briefing about the nuclear protocols, so too can the president be briefed about statistical reasoning and how to thereby make well-calibrated decisions on behalf of the nation.
Targeted practical statistical training
Economic Growth, Values and Reflective Processes
“Human cognition is characterized by cognitive biases, which systematically lead to errors in judgment: errors that can potentially be catastrophic (e.g., overconfidence as a cause of war). For example, a strong case can be made that Russia’s invasion of Ukraine has been an irrational decision of Putin, a consequence of which is potential nuclear war. Overconfidence is a cause of wars and of underpreparation for catastrophes (e.g., pandemics, as illustrated by the COVID-19 pandemic).
One way to reduce detrimental and potentially catastrophic decisions is to provide people with statistical training that can help empower beneficial decision-making via correct calibration of beliefs. (Statistical training to keep track of the mean past payoff/observation can be helpful in a general sense; see my paper on the evolution of human cognitive biases and implications.) At the moment, statistical training is provided to a very small percentage of people, and most provisions of statistical training are not laser-focused on the improvement of practical learning/decision-making capabilities, but for other indirect goals (e.g., prerequisite for STEM undergraduate majors). It may be helpful to (1) encourage practical, impactful aspects in the provision of statistical training and (2) broaden its provision to a wider segment of people.”
(Quote from my post ’Broadening statistical education”)
Given resource limitations, it may make sense to target the provision of practical statistics training to high-impact decision-makers, such as those in government. An ambitious example is that just as the US president is given a confidential briefing about the nuclear protocols, so too can the president be briefed about statistical reasoning and how to thereby make well-calibrated decisions on behalf of the nation.