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 (specifically survivor or attendance bias) can make the smokers who show up at a 20th high‑school reunion look healthier than nonsmokers, even if smoking increases cancer risk.

Relevant knowledge points and concepts:
- Selection bias: the sample of reunion attendees may not be representative of the original class.
- Survivor bias: smokers who developed cancer or died before the reunion are less likely to attend, leaving a healthier subset.
- Confounding and sampling error: other differences (age, socioeconomic status, health behaviors) could also affect observed cancer rates.
- Proper inference requires checking representativeness or using study designs (cohort studies, mortality records) that avoid attendance bias.

Step‑by‑step reasoning:
1. Observe the surprising finding: smokers at the reunion have a lower observed cancer rate than nonsmokers.
2. Ask whether the reunion sample reflects the original cohort. If many smokers with cancer are absent (dead, too ill, or unwilling to attend), the attending smokers will be a nonrandom, healthier group.
3. Check attendance by smoking status: compare the proportion of original smokers who attended to the proportion of original nonsmokers who attended. If fewer smokers attended, the attendees are a biased sample.
4. If attendance differs, the reunion comparison is invalid for inferring the causal effect of smoking on cancer. Instead, use more complete data (original cohort follow‑up, death/cancer registries) or adjust analytically for selection.
5. Only after ruling out selection bias and other confounders should you interpret observed differences.

Why the provided choice is correct:
- It directly addresses the most plausible explanation for the paradoxical finding: differential attendance. Verifying attendance proportions will reveal whether observed rates are biased by who showed up.

Why other inferences would be incorrect:
- Concluding smoking prevents cancer ignores survivor/selection bias and confounding.
- Accepting reunion rates as population estimates ignores representativeness and possible missing data.

Conclusion: The most reasonable action is to check whether fewer smokers from the original class attended the reunion relative to nonsmokers. This matches the provided answer: "Before drawing a conclusion, check if fewer of your smoking classmates were present at the reunion, relative to your non-smoking classmates."

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