When faced with incomplete data, it's really always better to go back and get a more complete and reliable dataset than to apply a host of statistical tools to that poor data in the hopes of extracting some trend or meaning from it.
> "Fortunately, you have a test that can detect poison very accurately, even among a large volume of liquid. Unfortunately, you have only 7 of these tests available. What should you do?"
You should estimate the cost of getting another 93 tests, consider if a cheaper method is plausible and could be developed at lower cost, then compare that to the value of the wine bottles.
With infectious disease, this is problematic as putting a cost on every individual human life is tricky, even if that's the life insurance business model. As a practical example, the cost of running the CDC's malaria detection program (captures some 2,000-3,000 cases a year from incoming travellers) in the USA is considered worth the benefit of not allowing malaria to become endemic again across the Southeastern US.
> "Fortunately, you have a test that can detect poison very accurately, even among a large volume of liquid. Unfortunately, you have only 7 of these tests available. What should you do?"
You should estimate the cost of getting another 93 tests, consider if a cheaper method is plausible and could be developed at lower cost, then compare that to the value of the wine bottles.
With infectious disease, this is problematic as putting a cost on every individual human life is tricky, even if that's the life insurance business model. As a practical example, the cost of running the CDC's malaria detection program (captures some 2,000-3,000 cases a year from incoming travellers) in the USA is considered worth the benefit of not allowing malaria to become endemic again across the Southeastern US.