The

The amazing and counter-intuitive thing about the central limit theorem is that no matter the shape of the original distribution, x-bar approaches a normal distribution.

- If the original variable X has a normal distribution, then x-bar will be normal regardless of the sample size.
- If the original variable X does not have a normal distribution, then x-bar will be normal only if N ≥ 30. This is called a distribution-free result. This means that no matter what distribution X has, it will still be normal for sufficiently large n.

Keep in mind that

Two things should be noted about the effect of increasing N:

- The distributions become more and more normal.
- The spread of the distributions decreases.

Based on the central limit theorem, when the sample size is large, you can:

- use the sample mean to infer the population mean.
- construct confidence intervals for the population mean based on the normal distribution.

Note that the central limit theorem does not prescribe that the underlying population must be normally distributed. Therefore, the central limit theorem can be applied to a population with any probability distribution.

jayj001: SAMPLE SIZE = no. of observations in each samplee.g. 100 samples with 5 observations in each NOT normal distribution as N < 30 |

Bududeen: it depends if the distribution itself is not normal...if the distribution is normal , then the sample size does not affect the normality of the distributionso even with a sample size of 5, if the distribution is normal then the distribution follows a normal dist. |

GouldenOne: What is variable X? |

sgossett86: Basically we're navigating from initial statistic theory to how to take our own imperfect data and make standard probability distributions so we can standardize and make inferences comparatively as an analyst. |