The amount of error created by applying this theorem to intermediate values is the subject of the
 Berry-Esseen theorem. The Cramer-Wold theorem can be used to generalize this
The amount of error created by applying this theorem to intermediate values is the subject of the
 Berry-Esseen theorem. The Cramer-Wold theorem can be used to generalize this in a multivariate manner.
 The de Moivre-Laplace theorem is a special case of this. One statement of this is named for Feller and
 Lindeberg and this is true when Lyapunov's condition holds. A rule of thumb resulting from this is that one
 should have a minimum of 30 in a sample when the population is non-Gaussian. When sampling, this
 theorem implies a distribution with standard deviation sigma over square root n. For 10 points, name this
 theorem that implies as sample sizes get larger, the distribution of sample means approaches a normal
 distribution.
The amount of error created by applying this theorem to intermediate values is the subject of the
 Berry-Esseen theorem.
{central limit} theorem