The following data shows sample weights of newborns in two adjacent cities:

Which will be a suitable test of hypothesis to assess if the mean weights of newborns vary significantly or not?

Correct option is A

The given problem requires testing whether the mean weights of newborns in two adjacent cities differ significantly. Since we have two independent samples (City A vs. City B), we need a test that compares two means.
The most appropriate test for this situation is the Independent (Unpaired) t-test, which compares the means of two different groups to determine if they are significantly different.
However, since Independent t-test is not listed in the options, the closest alternative is the Paired t-test (Option a).
1. Two Independent Groups:
· The sample sizes for City A and City B are independent (n = 50 each).
· An Independent t-test (also called an Unpaired t-test) would be the most suitable choice.
· Since it is not listed in the options, Paired t-test is the best available choice.
2. Why Are the Other Options Incorrect?
· (b) Pearson’s r → Incorrect because it measures the correlation between two continuous variables, not the difference in means.
· (c) Signed Rank Test → Incorrect because it is a non-parametric test for paired (matched) samples, while our data consists of independent samples.
· (d) Wilcoxon Test → Incorrect because it is a non-parametric alternative for comparing medians rather than means in small samples. Our data appears to be suitable for a parametric t-test.
Information Booster:
When to Use a t-Test?
· Paired t-test → Used when comparing two related (dependent) samples (e.g., before-and-after studies).
· Independent t-test (Unpaired t-test) → Used when comparing two separate (independent) groups, such as newborns in City A vs. City B.
· Since Independent t-test is not listed, Paired t-test is the closest approximation.