Select True or False from each pull-down menu, depending on whether the corresponding statement is true or false.

? True False  1. The central limit theorem is basic to the concept of statistical inference because it permits us to draw conclusions about the population based strictly on sample data, and without having any knowledge about the distribution of the underlying population.

? True False  2. When a great many simple random samples of size $n$ are drawn from a population that is normally distributed, the sampling distribution of the sample means will be normal regardless of the sample size $n$.

? True False  3. If all possible samples of size $n$ are drawn from a population, the probability distribution of the sample mean $\bar{X}$ is referred to as the normal distribution.

? True False  4. As a general rule, the normal distribution is used to approximate the sampling distribution of the sample proportion only if the sample size $n$ is greater than or equal to 30.