In a ten-year study, one group of volunteers was given a medical screening for disease X every year, whereas an otherwise similar group of the same size was only screened for disease X at the end of the study. Nine percent of the first group were diagnosed with disease X during the study and received treatment, but only six percent of the second group were diagnosed with disease X when they received the screening at the end of the study. The researchers concluded that during the ten-year period, disease X must have disappeared without medical treatment in some individuals in the second group.
In order to evaluate the strength of the researcher’s reasoning, it would be most helpful to know which of the following?
- A. Whether there were statistically significant lifestyle differences between the individuals who were diagnosed with disease X and those who were not
- B. How many people volunteered for the study because they knew that they had an especially high risk for disease X
- C. How long it takes to be treated for disease X
- D. Whether volunteers were told what disease they were being screened for
- E. How frequently on average the medical screening used in the study produces erroneous diagnoses of disease X
Solution
Passage Analysis:
| Text from Passage | Analysis |
| In a ten-year study, one group of volunteers was given a medical screening for disease X every year, whereas an otherwise similar group of the same size was only screened for disease X at the end of the study. | What it says:Ā Sets up a study comparing yearly screening vs. end-of-study screening for disease X What it does:Ā Establishes the basic experimental design and comparison framework What it is:Ā Study methodology description Visualization:Ā Group 1: ???????????????????????????????????????? (screened every year for 10 years) Group 2: _ _ _ _ _ _ _ _ _ ???? (screened only at year 10) |
| Nine percent of the first group were diagnosed with disease X during the study and received treatment, but only six percent of the second group were diagnosed with disease X when they received the screening at the end of the study. | What it says:Ā Group 1 had higher diagnosis rates (9%) than Group 2 (6%) What it does:Ā Presents the key finding that builds on the study setup – shows different outcomes between groups What it is:Ā Study results/data Visualization:Ā Group 1 (yearly screening): 9 out of 100 people diagnosed ???????????????????????????????????? Group 2 (end screening): 6 out of 100 people diagnosed ???????????????????????? |
| The researchers concluded that during the ten-year period, disease X must have disappeared without medical treatment in some individuals in the second group. | What it says:Ā Researchers believe some people in Group 2 naturally recovered from disease X What it does:Ā Provides the researchers’ interpretation of the data difference as evidence of natural recovery What it is:Ā Researchers’ conclusion/interpretation |
Argument Flow:
The argument starts by describing a study design that compares two screening approaches, then presents the numerical results showing different diagnosis rates, and finally offers the researchers’ explanation for why the rates differed.
Main Conclusion:
Disease X must have disappeared naturally without medical treatment in some people from the group that was only screened at the end of the study.
Logical Structure:
The researchers use theĀ 3%Ā difference in diagnosis rates (9% vs 6%) as evidence that some people in the second group naturally recovered from disease X during the ten-year period. They assume the lower rate in Group 2 means people had the disease but it went away on its own before the final screening.
Prethinking:
Question type:
Evaluate – We need to find information that would help us judge whether the researchers’ conclusion is valid or not
Precision of Claims
The researchers make a specific quantitative claim about disease disappearance based on aĀ 3%Ā difference in diagnosis rates (9% vsĀ 6%) between two screening approaches
Strategy
For evaluate questions, we need to think of assumptions underlying the conclusion and create scenarios that would either strengthen or weaken the reasoning when we get more information. The researchers assume theĀ 3%Ā difference means disease X disappeared naturally in some people from group 2. We should think of alternative explanations for this difference that would make us question or support this conclusion.
Answer Choices Explained
A. Whether there were statistically significant lifestyle differences between the individuals who were diagnosed with disease X and those who were not
Whether there were statistically significant lifestyle differences between diagnosed and non-diagnosed individuals doesn’t help us evaluate the researchers’ specific conclusion about disease disappearance. The researchers are comparing two groups with different screening schedules, not comparing diagnosed vs non-diagnosed individuals within groups. Lifestyle differences wouldn’t explain why the same disease would disappear naturally in one group versus another.
B. How many people volunteered for the study because they knew that they had an especially high risk for disease X
Knowing how many people volunteered because of high risk doesn’t help evaluate the conclusion. Even if some volunteers had higher risk, this would affect both groups equally since they’re described as ‘otherwise similar.’ This information doesn’t address whether theĀ 3%Ā difference in diagnosis rates supports the conclusion about natural disease disappearance.
C. How long it takes to be treated for disease X
How long treatment takes is irrelevant to evaluating whether disease X disappeared naturally. The researchers’ conclusion focuses on what happened in the untreated second group before they received any screening. Treatment duration doesn’t help us assess whether the lower diagnosis rate in group 2 truly indicates natural recovery.
D. Whether volunteers were told what disease they were being screened for
Whether volunteers knew what disease they were being screened for might affect their behavior, but this doesn’t help evaluate the core reasoning. Both groups would be equally affected by this knowledge, so it doesn’t explain theĀ 3%Ā difference in diagnosis rates or help us assess whether this difference supports the natural disappearance conclusion.
E. How frequently on average the medical screening used in the study produces erroneous diagnoses of disease X
This is exactly what we need to evaluate the researchers’ reasoning. If the medical screening frequently produces false positive diagnoses, then theĀ 9% rate in group 1 (yearly screening) could be artificially inflated with incorrect diagnoses, making theĀ 3%Ā difference less meaningful as evidence of natural disease disappearance. If the screening is highly accurate, then the difference becomes more significant support for the conclusion. This information directly helps us assess whether the observed difference truly indicates natural recovery or could be explained by screening errors.
