You use the central limit theorem when you are performing statistical calculations and are assuming the data is normally distributed. In many cases, this assumption can be made provided the sample size is large enough.
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Yes, and the justification comes from the Central Limit Theorem.
This is the Central Limit Theorem.
The central limit theorem is one of two fundamental theories of probability. It's very important because its the reason a great number of statistical procedures work. The theorem states the distribution of an average has the tendency to be normal, even when it turns out that the distribution from which the average is calculated is definitely non-normal.
The Central Limit Theorem states that the sampling distribution of the sample means approaches a normal distribution as the sample size gets larger — no matter what the shape of the population distribution. This fact holds especially true for sample sizes over 30.
According to the Central Limit Theorem, the mean of a sufficiently large number of independent random variables which have a well defined mean and a well defined variance, is approximately normally distributed.The necessary requirements are shown in bold.According to the Central Limit Theorem, the mean of a sufficiently large number of independent random variables which have a well defined mean and a well defined variance, is approximately normally distributed.The necessary requirements are shown in bold.According to the Central Limit Theorem, the mean of a sufficiently large number of independent random variables which have a well defined mean and a well defined variance, is approximately normally distributed.The necessary requirements are shown in bold.According to the Central Limit Theorem, the mean of a sufficiently large number of independent random variables which have a well defined mean and a well defined variance, is approximately normally distributed.The necessary requirements are shown in bold.