What? No. That's not how this works. First, I'm assuming that top comment meant the false-positive rate was 1%, even though it was poorly stated as "accuracy."

What? No. That's not how this works. First, I'm assuming that top comment meant the false-positive rate was 1%, even though it was poorly stated as "accuracy." If you're commenting on this original ambiguity, then the whole thing is ambiguous. But if we accept a 1% false-positive rate, then it makes no difference at all what the false-negative rate is, once we have a positive test. Out of a 100,000 tests, on average 1000 will be falsely positive. That's what a 1% false-positive means. It makes no difference how many are truly positive, how many are truly negative, and how many are falsely negative. The definition of 1% false-positive means that, out of 100,000 tests, on average 1000 will show a positive test when the condition isn't actually there. So if 100,000 people each took two tests, on average 10 will have two false-positive tests. Again, this is independent of the number of false-negatives, and is simply the definition of false-positive. (Some tests may have correlated false-positives, i.e. you're more likely to get two in a row, but that's not standard and not part of the definition.) So if you have a disease whose incidence is 10 people out of 100,000, and you took two tests which are both positive, you are equally-likely to be in the group of 10 people who really have the disease, and the group of 10 people sitting there with two positive tests who don't have the disease. You seem to be saying that the percentages will be affected by whether any of the 10 out of 100,000 who really do have the disease got a negative test. But it doesn't. 1% false-positive means that, on average, 10 people who don't have the disease will get two false positive tests. It doesn't matter what the people who really have it get.