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Central Limit Theorem Definition. The Central Limit Theorem (CLT) states that the distribution of a sample mean that approximates the normal distribution, as the sample size becomes larger, assuming that all the samples are similar, and no matter what the shape of the population distribution. Central Limit Theorem Example.
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The central limit theorem states that if data is independently drawn from any distribution and the sample size is large enough, the sample mean always appears to be normally distributed. This might be a little difficult to comprehend at the moment so let’s take a look at the sample mean and its properties.
Central Limit Theorem for the Mean and Sum Examples. Example 3.9. A study involving stress is done on a college campus among the students.. Most school labs have Microsoft Excel, an example of computer software that calculates binomial probabilities.
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Central Limit Theorem. One of the most important parts of statistics and probability theory is the Central Limit Theorem. The central limit theorem states that if some certain conditions are satisfied, then the distribution of the arithmetic mean of a number of independent random variables approaches a normal distribution as the number of variables approaches infinity.