The central limit theorem

states that if the sample size, 

, is sufficiently large, the

sampling distribution of the means will be approximately normal, even when the

population is skewed or uniform.

The size of the sampling

error is inversely

related to the population standard deviation—in other words, the smaller the

standard deviation, the larger the sampling error.

The size of the sampling error is directly

related to the sample size—in other words, the larger the sample size, the

larger the sampling error.