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In tests for pironoma, a serious disease, a false positive result indicates that people have pironoma when, in fact, they do not; a false negative result indicates that people do not have pironoma when, in fact, they do. To detect pironoma most accurately, physicians should use the laboratory test that has the lowest proportion of false positive results.
Which of the following, if true, gives the most support to the recommendation above?
| Text from Passage | Analysis |
| In tests for pironoma, a serious disease, a false positive result indicates that people have pironoma when, in fact, they do not; a false negative result indicates that people do not have pironoma when, in fact, they do. |
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| To detect pironoma most accurately, physicians should use the laboratory test that has the lowest proportion of false positive results. |
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"The argument first defines the two types of testing errors, then immediately jumps to a recommendation that focuses only on minimizing false positives without explaining why false negatives don't matter."
"Physicians should use the laboratory test that has the lowest proportion of false positive results to detect pironoma most accurately."
"The argument provides definitions as background, then states a conclusion about test selection. However, there's a logical gap - we're told about both false positives AND false negatives, but the recommendation only considers false positives. The argument doesn't explain why minimizing false positives (rather than false negatives or both) leads to the most accurate detection."
Strengthen - We need to find information that makes the recommendation (use tests with lowest false positive rates) more believable and well-founded
The recommendation is about test selection strategy - specifically choosing tests based on minimizing one type of error (false positives) while ignoring the other type (false negatives)
Since the author recommends focusing only on false positive rates, we need to strengthen this by showing why false positives are more problematic than false negatives, or why minimizing false positives leads to better overall accuracy. We should look for scenarios that justify prioritizing false positive reduction over false negative reduction in the context of pironoma testing
This choice tells us that pironoma treatment doesn't have damaging side effects. While this might make false positives less harmful (since unnecessary treatment wouldn't hurt patients), it doesn't strengthen the core recommendation about test selection strategy. The argument is about choosing the most accurate test, not about treatment consequences. This doesn't address why we should focus on false positive rates rather than false negative rates or overall accuracy.
This choice discusses side effects of the laboratory tests themselves, stating that the recommended test (lowest false positives) has the same minor side effects as other tests. This information is irrelevant to the argument's logic about accuracy and test selection. We're concerned with diagnostic accuracy, not procedural side effects. This doesn't help justify why minimizing false positives leads to better detection.
This choice emphasizes the critical importance of early treatment for pironoma patients. If early treatment is essential and delays can be fatal, this would actually strengthen an argument for minimizing false negatives (missing sick people), not false positives. This choice works against the given recommendation since false negatives would delay necessary treatment, making them more problematic than false positives.
This states that inconclusive results are equal across all tests. While this removes one variable from test comparison, it doesn't address the key issue of why we should prioritize false positive rates over false negative rates. Inconclusive results are a third category that doesn't help us understand the relationship between the two main types of errors the argument discusses.
This directly strengthens the argument by stating that all laboratory tests have the same proportion of false negative results. If false negative rates are constant across all available tests, then the only meaningful way to differentiate between tests is by their false positive rates. This makes the recommendation logical - since we can't improve false negative performance (it's the same everywhere), we should choose tests based on minimizing false positives. This perfectly fills the logical gap in the original argument.