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. One upside is that policies that voters or politicians choose may become more prudent and consistent with empirical evidence.
One ambitious course of action is to (1) design a curriculum for practical statistics training designed for the median (say American) high-school student and (2) advocate for the use of this curriculum in education. This may have the secondary benefit of getting more young students interested in longtermism and effective altruism. A general goal that can be pursued is to increase the importance of practical data science in high-school and undergraduate education, the room for which can be made for example by making subjects like Euclidean geometry optional.
What I think I’d love to see is one of the below: - statistics bootcamps - statistics tutoring (or more like lack of problems to work on with your tutor, my idea was to try and go through actuary exam questions) - something like Cochrane Training (where you can learn interventions review) but more broad/general?
Thanks so much for these suggestions! I would also really like to see these projects get implemented. There are already bootcamps for, say, pivoting into data science jobs, but having other specializations of statistics bootcamps (e.g., an accessible life-coach level bootcamp for improving individual decision-making, or a bootcamp specifically for high-impact CEOs or nonprofit heads) could be really cool as well.
Broadening statistical education
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. One upside is that policies that voters or politicians choose may become more prudent and consistent with empirical evidence.
One ambitious course of action is to (1) design a curriculum for practical statistics training designed for the median (say American) high-school student and (2) advocate for the use of this curriculum in education. This may have the secondary benefit of getting more young students interested in longtermism and effective altruism. A general goal that can be pursued is to increase the importance of practical data science in high-school and undergraduate education, the room for which can be made for example by making subjects like Euclidean geometry optional.
What I think I’d love to see is one of the below:
- statistics bootcamps
- statistics tutoring (or more like lack of problems to work on with your tutor, my idea was to try and go through actuary exam questions)
- something like Cochrane Training (where you can learn interventions review) but more broad/general?
Thanks so much for these suggestions! I would also really like to see these projects get implemented. There are already bootcamps for, say, pivoting into data science jobs, but having other specializations of statistics bootcamps (e.g., an accessible life-coach level bootcamp for improving individual decision-making, or a bootcamp specifically for high-impact CEOs or nonprofit heads) could be really cool as well.