Central Limit Theorem
The central limit theorem is one of the most significant conclusions of statistics.
The central limit theorem gives us opportunity to use the normal distribution theory in order to create confidence interval of the population mean.
With a confidence interval we can with a certain assurance determine the population mean.
According to the central limit theorem, for a large sample size, the sampling distribution of the sample mean is approximately normal, irrespective of the shape of the population distribution. The sample size is usually considered to be large if the sample size is over 30.
Note that when the population does not have a normal distribution, the shape of the sampling distribution is not exactly normal but approximately normal for a large sample size. The approximation becomes more accurate as the sample size increases.
Thus, if the sample size is 30 or more, the sample mean can be regarded to follow a normal distribution irrespective of the shape of the population. This is a result of a very practical importance.