To test the claim that the proportion of babies born at low birth weight among women who smoked during their pregnancy is higher than the proportion in the general population, we need to set up the null and alternative hypotheses as follows:

Let $p_s$ represent the proportion of low birth weight babies among women who smoked, and $p_g$ represent the proportion of low birth weight babies in the general population.

Hypotheses:

• Null Hypothesis ($H_0$): $p_s = p_g$
  This hypothesis states that there is no difference between the two proportions, implying that smoking during pregnancy does not lead to a higher proportion of low birth weight babies than in the general population.

• Alternative Hypothesis ($H_1$): $p_s > p_g$
  This hypothesis suggests that the proportion of low birth weight babies is higher among women who smoked during pregnancy than in the general population.

These hypotheses are designed for a one-tailed test at the 0.05 significance level.

Would you like further details on setting up the hypothesis test?

Here are some related questions:

1. How do you interpret a one-tailed test versus a two-tailed test?
2. What are the implications of the 0.05 significance level in hypothesis testing?
3. How would you calculate the test statistic for this hypothesis?
4. What is the meaning of a Type I and Type II error in this context?
5. How do you determine if the sample size is adequate for a proportion hypothesis test?

Tip: When setting hypotheses, always make sure the null hypothesis represents the status quo or no effect, while the alternative represents the research claim.