Step 1 :The question is asking why a large sample size is necessary when the histogram of time spent eating and drinking each day is skewed right. This is a theoretical question and does not require any calculations.
Step 2 :The Central Limit Theorem states that if you have a population with mean μ and standard deviation σ and take sufficiently large random samples from the population with replacement, then the distribution of the sample means will be approximately normally distributed.
Step 3 :This will hold true regardless of the shape of the population distribution. Therefore, when the population distribution is skewed right, a larger sample size is needed to ensure that the sample mean distribution is approximately normal.
Step 4 :This is important for constructing a confidence interval for the mean time spent eating and drinking each day.
Step 5 :Therefore, the answer is A. Since the distribution of time spent eating and drinking each day is not normally distributed (skewed right), the sample must be large so that the distribution of the sample mean will be approximately normal.
Step 6 :\(\boxed{\text{A. Since the distribution of time spent eating and drinking each day is not normally distributed (skewed right), the sample must be large so that the distribution of the sample mean will be approximately normal.}}\)