The mean of a data set is observed to be very different from its median, representing a strong skewness. However, the 1.5 IQR rule reveals that there are no outliers. Which of the following is correct, if the sample size is 100?

a. A normal quantile plot of the data follows a diagonal line, and the t-procedure is appropriate to use.
b. A normal quantile plot of the data does not follow a diagonal line, and the t- procedure is not appropriate to use.
c. A normal quantile plot of the data follows a diagonal line, and the t-procedure is not appropriate to use.
d. A normal quantile plot of the data does not follow a diagonal line, and the t- procedure is appropriate to use.

The Central Limit Theorem establishes that, for a normally distributed random variable X, with mean [tex]\mu[/tex] and standard deviation [tex]\sigma[/tex], the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean [tex]\mu[/tex] and standard deviation [tex]s = \frac{\sigma}{\sqrt{n}}[/tex].

For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.

For a proportion p in a sample of size n, the sampling distribution of the sample proportion will be approximately normal with mean [tex]\mu = p[/tex] and standard deviation [tex]s = \sqrt{\frac{p(1-p)}{n}}[/tex]

In this question:

Sample size of 100 > 30, which means that we use the Central Limit Theorem, and thus, the sampling distribution is approximately normal, following a diagonal line, and since the standard deviation of the population is not know, we use the t-procedure. Thus, the correct answer is given by option a.