Central limit theory
I need one paragraph explaining the conditions for the Central limit theorem.
Answer preview
The Central limit theory is a statistical supposition applied with an adequately large sample size from a finite variance population. It has several conditions, such as the randomization condition. According to this assumption, the sampling methodologies must be sampled randomly. Another condition for this theorem is the independence assumption. It states that the sample values utilized should be independent of each other. Therefore, the occurrence of one event should not influence another. The independence assumption is usually met when the sample has been selected randomly. Individuals assigned in a random sample are entirely by chance; thus, they should not impact other values. The Central limit theorem also holds a 10% condition. Based on this assumption, the sample needs to be drawn without replacement, and it should not exceed 10% of the population selected. The last condition of this theorem is that the sample size must be adequately large. However, the central limit theorem does not define how large the size should be, although a standard model is recommended for a large enough sample size. The theory can only be applied with a sufficiently large sample, especially with a skewed population.
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