Step 1: Understanding the Concept:
This question asks us to find a statement that would weaken or undermine the researchers' conclusion. The conclusion is that dental disease in children declined from 1970 to 1985. The evidence is that the percentage of children who had ever had a cavity dropped from 70% to 50%. A strong weakener will show that the comparison between the 1970 and 1985 groups is flawed, providing an alternative explanation for the drop in the statistic.
Step 2: Detailed Explanation:
Let's analyze the argument's structure:
- Conclusion: Dental health improved (disease declined).
- Evidence: The percentage of children who have ever had a cavity decreased.
- Assumption: The two groups of children (1970 vs. 1985) are comparable in all relevant aspects other than the time period.
Now let's evaluate the options:
- (A) This strengthens the conclusion by suggesting that using cavities as a measure for overall dental disease is appropriate.
- (B) This strengthens the conclusion by suggesting the survey was representative and well-conducted.
- (C) This describes the sampling method but does not inherently weaken the conclusion unless a specific bias is identified.
- (D) If detection techniques improved, it means dentists in 1985 were better at finding cavities than in 1970. If the reported rate still went down despite better detection, it suggests the actual decline in cavities was even greater than observed. This would strengthen, not weaken, the conclusion.
- (E) This is a very strong weakener. The statistic "had at one time had cavities" is cumulative. The longer a person has lived, the more time they've had to develop a cavity. If the 1985 group was younger on average, one would naturally expect a lower percentage of them to have ever had a cavity, even if the actual annual rate of getting cavities had not changed or had even increased. This age difference provides a powerful alternative explanation for the statistical finding, undermining the conclusion that dental disease had actually declined.
Step 3: Final Answer:
The difference in average age between the two survey groups breaks the comparability of the data and provides a confounding variable that could explain the observed results, thus seriously undermining the researchers' conclusion.