“The right question” has 2 components. First is that the thing you’re asking about is related to what you actually want to know, and second is that it’s a clear and unambiguously resolvable target. These are often in tension with each other.
One clear example is COVID-19 cases—you probably care about total cases much more than confirmed cases, but confirmed cases are much easier to use for a resolution criteria. You can make more complex questions to try to deal with this, but that makes them harder to forecast. Forecasting excess deaths, for example, gets into whether people are more or less likely to die in a car accident during COVID-19, and whether COVID reduction measures also blunt the spread of influenza. And forecasting retrospective population percentages that are antibody positive runs into issues with sampling, test accuracy, and the timeline for when such estimates are made—not to mention relying on data that might not be gathered as of when you want to resolve the question.
“The right question” has 2 components. First is that the thing you’re asking about is related to what you actually want to know, and second is that it’s a clear and unambiguously resolvable target. These are often in tension with each other.
One clear example is COVID-19 cases—you probably care about total cases much more than confirmed cases, but confirmed cases are much easier to use for a resolution criteria. You can make more complex questions to try to deal with this, but that makes them harder to forecast. Forecasting excess deaths, for example, gets into whether people are more or less likely to die in a car accident during COVID-19, and whether COVID reduction measures also blunt the spread of influenza. And forecasting retrospective population percentages that are antibody positive runs into issues with sampling, test accuracy, and the timeline for when such estimates are made—not to mention relying on data that might not be gathered as of when you want to resolve the question.