It's helpful to make claims about likely effectiveness quantitative. Instead of "boosters likely work", how about "boosters likely reduce hospitalizations/etc by at least X%"?
Instead of "masks should work in schools", "I think masks reduce cases in schools by at least X%".
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Instead of "masks should work in schools", "I think masks reduce cases in schools by at least X%".
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A claim you think something works, without quantifying how well you think it works, is barely even scientific, since it fails to be falsifiable; one can always disclaim negative findings as underpowered to find smaller and smaller effects.
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Being quantitative about beliefs is prerequisite to studying them well. If you believe an intervention may have a 90% benefit for a measurable target, observational studies may be able to lend strong support, even if we might look to large RCTs to support an expected 10% effect.
Just as importantly, being quantitative about what we believe can clarify the real sources of disagreement.
Do persons A and B disagree about an intervention disagree irreconcilably about the range of possible benefit? Or the range of possible harm? (Or neither?)
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Do persons A and B disagree about an intervention disagree irreconcilably about the range of possible benefit? Or the range of possible harm? (Or neither?)
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By being more quantitative, we can better understand our disagreements, be better equipped to study them scientifically, and better understand our own beliefs.
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