Wheelan suggest that if you went to your 20th high school reunion you might discover that the smokers at the reunion have a lower rate of cancer than the nonsmokers there. If you were interested in the relationship between smoking and cancer, what would be the most reasonable course of action?

Question

BlackTom AI · Accepted Answer

Selection bias (including survivorship bias) is the key concept: the apparent lower cancer rate among smokers at a reunion can be an artifact of who showed up, not evidence that smoking protects against cancer.

Relevant knowledge points:
- Selection bias occurs when the sample observed is not representative of the whole population because participation is related to both exposure (smoking) and outcome (cancer).
- Survivorship bias: smokers who developed cancer or died earlier are less likely to attend a reunion, so the attending smokers may be a healthier subset.
- Confounding: other differences between smokers and nonsmokers (age, socioeconomic status, healthcare access) can distort apparent associations.
- Proper inference requires representative sampling or a study design (cohort, case-control) that accounts for these biases.

Step-by-step reasoning:
1. Observe the paradox: at the reunion, smokers seem to have lower cancer rates than nonsmokers.
2. Ask how the sample was formed: who attended the reunion? Attendance is not random; ill or deceased classmates (potentially more smokers) might be underrepresented.
3. If fewer smokers attended relative to nonsmokers—especially if smokers with cancer were less likely to come—then the attendees’ smoking group will appear healthier than the true underlying group.
4. Therefore, before concluding anything about causation or protective effects, verify attendance patterns and ascertain whether the reunion sample is biased.
5. Ideally, use population-level data or an epidemiological study that tracks all classmates (or a randomly sampled cohort) and adjusts for confounders.

Why the correct answer is correct:
Checking whether fewer smokers attended directly addresses selection/survivorship bias and is the most reasonable first step to assess whether the observed difference reflects reality or sampling error.

Why other responses would be wrong:
- Jumping to the conclusion that smoking reduces cancer confuses correlation with causation and ignores known risks.
- Treating a single anecdotal reunion as definitive ignores representativeness and confounding.
- Claiming no action is needed ignores the potential for biased inference.

Conclusion: Confirming attendance differences (fewer smokers present) is the appropriate initial action; this matches the provided correct answer.

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