Question at position 22 In order to assess the effects of exercise on reducing cholesterol, a researcher took a random sample of fifty people from a local gym who exercised regularly and another random sample of fifty people from the surrounding community who did not exercise regularly. They all reported to a clinic to have their cholesterol measured. The subjects were unaware of the purpose of the study, and the technician measuring the cholesterol was not aware of whether or not test subjects exercised regularly. Which of the following best describes the inferences the researcher can make based on his results?He cannot make inferences about either cause and effect or the populations from which the samples were taken.He can make inferences about the populations from which the samples were taken, but not about cause and effect.He can make inferences about both cause and effect and the populations from which the samples were taken.There is not enough information to make judgments about the scope of inference.He can make inferences about cause and effect, but not about the populations from which the samples were taken.

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BlackTom AI · Accepted Answer

To tackle this question, let's lay out what the study design allows and what it limits. The researchers drew two independent random samples: fifty exercisers from a gym and fifty non-exercisers from the surrounding community. The key feature is that participants were not randomly assigned to exercise or not; their prior behavior determined group membership. The measurements (cholesterol) were taken by a blinded technician, which helps reduce measurement bias, but it does not fix issues of causality.

Option 1: 'He cannot make inferences about either cause and effect or the populations from which the samples were taken.' This overstates the limits. Because the samples were drawn randomly from defined populations (exercisers and non-exercisers), the results can be generalized to those specific populations, provided the samples are representative and other assumptions hold. So this option is too pessimistic.

Option 2: 'He can make inferences about the populations from which the samples were taken, but not about cause and effect.' This aligns with the core principle: since assignment to exercise was not random, the study is observational, not experimental. We can describe associations and generalize to the populations sampled, but we cannot claim that exercise caused the cholesterol differences.

Option 3: 'He can make inferences about both cause and effect and the populations from which the samples were taken.' This would require random assignment to conditions to support causal inferences. Because participants were not randomized to exercise or not, causal conclusions are not warranted.

Option 4: 'There is not enough information to make judgments about the scope of inference.' The described design provides enough information to determine scope: population inferences are possible for the sampled groups, while causal inferences are not. So this option is unnecessarily vague.

Option 5: 'He can make inferences about cause and effect, but not about the populations from which the samples were taken.' This flips the truth: causal inferences are not supported by this observational setup, while population-level inferences for the sampled groups are appropriate. So this is incorrect.

In summary, the reasoning supports that the study allows generalization to the populations from which the samples were drawn (the exercisers and the non-exercisers), but it does not support claims of causality between exercise and cholesterol levels due to the lack of random assignment and potential confounding factors. The remaining options fail for either overstating causal claims, mischaracterizing the scope, or being too vague.

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