In A Study Of The Impact Of Smoking On Birth Weight ✓ Solved

In a study of the impact of smoking on birth weight, res

In a study of the impact of smoking on birth weight, researchers analyze birth weights (in grams) for babies born to 189 women who gave birth in 1989 at a hospital in Massachusetts. In the group, 74 of the women were categorized as “smokers” and 115 as “non-smokers.” The difference in the two sample mean birth weights (non-smokers minus smokers) is 281.7 grams and the 95% confidence interval is (76.5, 486.9). Which gives the best interpretation of what we can conclude about the impact of smoking on birth weight?

A. We are 95% confident that on average, smoking causes lower birth weights of between 76.5 grams to 486.9 grams.

B. There is a 95% chance that if a woman smokes during pregnancy her baby will weigh between 76.5 grams to 486.9 grams less than if she did not smoke.

C. Smoking is associated with lower birth weights. When smokers are compared to nonsmokers, we are 95% confident that the mean weight of babies of non-smokers is between 76.5 grams to 486.9 grams more than the mean weight of babies of smokers.

D. This study does not suggest that there is a difference in mean birth weights when we compare smokers to non-smokers.

Paper For Above Instructions

The relationship between smoking during pregnancy and birth weight has been the subject of numerous studies due to its critical implications for maternal and infant health. In this analysis, we focus on the findings of a particular study conducted in 1989 at a hospital in Massachusetts, which involved 189 women giving birth, categorized into smokers and non-smokers. The study reported a significant difference in birth weights between the two groups, showing that non-smokers had an average weight that was 281.7 grams higher than that of smokers. With a 95% confidence interval ranging from 76.5 grams to 486.9 grams, we aim to interpret these findings to elucidate the effects of smoking on birth weight.

To interpret the results appropriately, we first need to understand confidence intervals. A confidence interval provides a range in which we can expect the true population parameter (in this case, the mean difference in birth weight) to lie. Here, the 95% confidence interval of (76.5, 486.9) grams suggests that we are 95% confident the true mean difference in birth weights between babies born to non-smokers and smokers lies within this range. This confidence limit indicates that while there is clear evidence showing that non-smokers tend to have babies with higher average birth weights, the exact degree of that difference can fluctuate. It captures a potential decrease in birth weight as much as 76.5 grams and an upper limit of up to 486.9 grams.

Among the provided interpretations, option A states, "We are 95% confident that on average, smoking causes lower birth weights of between 76.5 grams to 486.9 grams." While the confidence interval does suggest that lower birth weights are associated with smoking, this statement somewhat misinterprets the data by asserting causation. It is crucial to recognize that while the study implies an association between smoking and lower birth weights, it does not conclusively establish a causal effect. Correlation does not imply causation, and therefore, we cannot definitively say that smoking causes lower birth weights. There may be other confounding factors such as socioeconomic status, prenatal care, or maternal nutrition influencing the birth weights.

Option B claims, "There is a 95% chance that if a woman smokes during pregnancy, her baby will weigh between 76.5 grams to 486.9 grams less than if she did not smoke." This interpretation also misrepresents the meaning of the confidence interval. The phrase "95% chance" is misleading in a statistical context. The confidence interval is not about the probability of an individual outcome, but rather about the estimate of the population parameter based on the sample data. This suggests that while the expected difference in birth weights lies within that range, it does not mean that each individual infant will experience that exact difference.

Option C acknowledges, "Smoking is associated with lower birth weights. When smokers are compared to non-smokers, we are 95% confident that the mean weight of babies of non-smokers is between 76.5 grams to 486.9 grams more than the mean weight of babies of smokers." This interpretation correctly highlights the association and clearly distinguishes it from a causal relationship. It accurately reflects the findings, emphasizing the comparative aspect of the birth weights while maintaining the statistical language's integrity, thus making it the most appropriate conclusion derived from the research.

Finally, option D, which states, "This study does not suggest that there is a difference in mean birth weights when we compare smokers to non-smokers," contradicts the data presented. The study's results unmistakably indicate a significant difference in mean birth weights based on maternal smoking status, thereby rendering this interpretation incorrect.

In conclusion, smoking during pregnancy correlates with an observed reduction in average birth weights. The correct interpretation from the given options is option C. It rightfully highlights the association while providing the necessary context of the confidence interval, further reminding us of the importance of understanding statistical data's nuances. Such findings emphasize the need for continued public health efforts to reduce smoking among pregnant women to improve maternal and infant health outcomes.

References

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• National Institutes of Health. (2017). Smoking and Birth Outcomes. Retrieved from http://www.nih.gov
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