Step 1: Understanding the Question:
The study is testing whether a drug lowers serum cholesterol. In each limb, obese women and non-obese women, the same set of women has their cholesterol measured before starting the drug and again after taking it. We want to know if the drug caused a real change, so we compare the two readings taken from the same person.
Step 2: Key Formula or Approach:
When two readings come from the same individual, such as cholesterol before and after a treatment in the same woman, the two values are related to each other rather than independent. This kind of related, before-after data needs a paired t test, which looks at the mean of the differences within each person. An unpaired t test is used only when the two sets being compared come from separate, independent groups of people, for example comparing a drug group against a completely different placebo group.
Step 3: Detailed Explanation:
Here, each woman acts as her own control: her cholesterol level before the drug is compared against her cholesterol level after the drug, within the same limb. This before-after design on the same subject is a paired setup, so a paired t test is the correct choice to correlate the pre and post drug values.
A chi square test is used to compare proportions or counts in categories, not continuous readings like cholesterol level, so it does not fit here.
Fischer's exact test is also for categorical data arranged in a small 2x2 table, and again does not apply to continuous cholesterol measurements.
An unpaired t test would only be the right test if we were comparing the obese group's readings against the non-obese group's readings as two separate, independent samples, which is a different comparison from the before-after change within each limb that the question is asking about.
Step 4: Final Answer:
Since the cholesterol readings before and after the drug come from the same women within each limb, the paired t test is the test used to correlate the results obtained.