The Central Limit Theorem
As I mentioned earlier, sample means behave in a very special way. According to the central limit theorem, as the sample size, n, gets larger, the sample means tend to follow a normal probability distribution. This holds true regardless of the distribution of the population from which the sample was drawn. Amazing, you say.
As you look at Figure 13.2, you’re probably scratching your head and thinking, “That distribution doesn’t look like a normal curve, which I know is bell-shaped and symmetrical.” You’re absolutely right because a sample size of two is generally not big enough for the central limit theorem to kick in.
Let’s satisfy your curiosity and repeat my experiment by gathering 25 samples each consisting ...